Probability and statistics Books
Cambridge University Press Analysis of Variance Designs A Conceptual and Computational Approach with SPSS and SAS
a huge range and FREE tracked UK delivery on ALL orders.
£85.49
Taylor & Francis Inc Stochastic Processes and Functional Analysis
Book SynopsisShows the effectiveness of abstract analysis for solving fundamental problems of stochastic theory, specifically the use of functional analytic methods for elucidating stochastic processes.Trade Review"More than 20 original papers reflect Rao's broad scientific interests in probability, stochastic processes, Banach space theory, measure theory and (stochastic) differential equations. …The volume is completed with a biography and bibliography of M. M. Rao, a remarkable collection of personal reminiscences (written by his former students) adds a human dimension to this fine book."-EMS Newsletter, June 2005Table of ContentsBiography of M. M. Rao, Published Writings of M. M. Rao, Ph.D. Theses Completed Under the Direction of M. M. Rao, Contributors, For M. M. Rao, An Appreciation of My Teacher, M. M. Rao, 1001 Words About Rao, A Guide to Life, Mathematical and Otherwise, Rao and the Early Riverside Years, On M. M. Rao, Reflections on M. M. Rao, 1: Stochastic Analysis and Function Spaces, 2: Applications of Sinkhorn Balancing to Counting Problems, 3: Zakai Equation of Nonlinear Filtering with Ornstein-Uhlenbeck Noise: Existence and Uniqueness, 4: Hyperfunctionals and Generalized Distributions, 5: Process-Measures and Their Stochastic Integral, 6: Invariant Sets for Nonlinear Operators, 7: The Immigration-Emigration with Catastrophe Model, 8: Approximating Scale Mixtures, 9: Cyclostationary Arrays: Their Unitary Operators and Representations, 10: Operator Theoretic Review for Information Channels, 11: Pseudoergodicity in Information Channels, 12: Connections Between Birth-Death Processes, 13: Integrated Gaussian Processes and Their Reproducing Kernel Hilbert Spaces, 14: Moving Average Representation and Prediction for Multidimensional Harmonizable Processes, 15: Double-Level Averaging on a Stratified Space, 16: The Problem of Optimal Asset Allocation with Stable Distributed Returns, 17: Computations for Nonsquare Constants of Orlicz Spaces, 18: Asymptotically Stationary and Related Processes, 19: Superlinearity and Weighted Sobolev Spaces, 20: Doubly Stochastic Operators and the History of Birkhoff s Problem 111, 21: Classes of Harmonizable Isotropic Random Fields, 22: On Geographically-Uniform Coevolution: Local Adaptation in Non-fluctuating Spatial Patterns, 23: Approximating the Time Delay in Coupled van der Pol Oscillators with Delay Coupling
£266.00
CRC Press Frontiers in Queueing
a huge range and FREE tracked UK delivery on ALL orders.
£120.00
Cambridge University Press Counterexamples in Measure and Integration
Book SynopsisOften it is more instructive to know ''what can go wrong'' and to understand ''why a result fails'' than to plod through yet another piece of theory. In this text, the authors gather more than 300 counterexamples - some of them both surprising and amusing - showing the limitations, hidden traps and pitfalls of measure and integration. Many examples are put into context, explaining relevant parts of the theory, and pointing out further reading. The text starts with a self-contained, non-technical overview on the fundamentals of measure and integration. A companion to the successful undergraduate textbook Measures, Integrals and Martingales, it is accessible to advanced undergraduate students, requiring only modest prerequisites. More specialized concepts are summarized at the beginning of each chapter, allowing for self-study as well as supplementary reading for any course covering measures and integrals. For researchers, it provides ample examples and warnings as to the limitations of general measure theory. This book forms a sister volume to René Schilling''s other book Measures, Integrals and Martingales (www.cambridge.org/9781316620243).Trade Review'This book is an admirable counterpart, both to the first author's well-known text Measures, Integrals and Martingales (Cambridge, 2005/2017), and to the books on counter-examples in analysis (Gelbaum and Olmsted), topology (Steen and Seebach) and probability (Stoyanov). To paraphrase the authors' preface: in a good theory, it is valuable and instructive to probe the limits of what can be said by investigating what cannot be said. The task is thus well-conceived, and the execution is up to the standards one would expect from the books of the first author and of their papers. I recommend it warmly.' N. H. Bingham, Imperial College'… an excellent reference text and companion reader for anyone interested in deepening their understanding of measure theory.' John Ross, MAA Reviews'… the unique nature of the book makes it an essential acquisition for any university with a doctoral program in pure mathematics … Essential.' M. Bona, Choice Connect'The book is well written, the demonstrations are clear and the bibliographic references are competent. We appreciate this work as extremely useful for those interested in measure theory and integration, starting with beginners and extending even to advanced researchers in the field.' Liviu Constantin Florescu, Mathematical Reviews/MathSciNet'Counterexamples in Measure and Integration is an ideal companion to help better understand canonically problematic examples in analysis … This collection of counterexamples is an excellent resource to researchers who rely on measure and integration theory. It would be helpful for students studying for their analysis qualifying exam as it draws on common misconceptions and enables readers to build intuition about why a given counterexample works and how conditions can be changed to make a particular statement hold.' Katelynn Kochalski, Notices of the AMS'This is a remarkable book covering Measure and Integration, perhaps one of the most important parts of Mathematics. It is written in a master style by following the best traditions in writing this kind of books. The authors are passionate about the topic. Look at the great care with which each of the counterexamples is presented. It is done in a way to help maximally the reader. The names of the counterexamples are chosen very carefully. Any name can be considered as a 'door' behind which is a treasure!' Jordan M. Stoyanov, zbMATH'… compendia of counterexamples remain a useful and thought-provoking resource, and this new text is a high-quality example in an analytic direction.' Dominic Yeo, The Mathematical GazetteTable of ContentsPreface; User's guide; List of topics and phenomena; 1. A panorama of Lebesgue integration; 2. A refresher of topology and ordinal numbers; 3. Riemann is not enough; 4. Families of sets; 5. Set functions and measures; 6. Range and support of a measure; 7. Measurable and non-measurable sets; 8. Measurable maps and functions; 9. Inner and outer measure; 10. Integrable functions; 11. Modes of convergence; 12. Convergence theorems; 13. Continuity and a.e. continuity; 14. Integration and differentiation; 15. Measurability on product spaces; 16. Product measures; 17. Radon–Nikodým and related results; 18. Function spaces; 19. Convergence of measures; References; Index.
£41.93
Cambridge University Press Probability and Statistics for Data Science
Book Synopsis
£47.99
Cambridge University Press Scalable Monte Carlo for Bayesian Learning
Book SynopsisA graduate-level introduction to advanced topics in Markov chain Monte Carlo (MCMC), as applied broadly in the Bayesian computational context. The topics covered have emerged as recently as the last decade and include stochastic gradient MCMC, non-reversible MCMC, continuous time MCMC, and new techniques for convergence assessment. A particular focus is on cutting-edge methods that are scalable with respect to either the amount of data, or the data dimension, motivated by the emerging high-priority application areas in machine learning and AI. Examples are woven throughout the text to demonstrate how scalable Bayesian learning methods can be implemented. This text could form the basis for a course and is sure to be an invaluable resource for researchers in the field.
£44.99
Cambridge University Press The ConwayMaxwellPoisson Distribution
£47.49
Taylor & Francis Ltd Psychological Statistics
Book SynopsisPsychological Statistics: The Basics walks the reader through the core logic of statistical inference and provides a solid grounding in the techniques necessary to understand modern statistical methods in the psychological and behavioral sciences. This book is designed to be a readable account of the role of statistics in the psychological sciences. Rather than providing a comprehensive reference for statistical methods, Psychological Statistics: The Basics gives the reader an introduction to the core procedures of estimation and model comparison, both of which form the cornerstone of statistical inference in psychology and related fields. Instead of relying on statistical recipes, the book gives the reader the big picture and provides a seamless transition to more advanced methods, including Bayesian model comparison.Psychological Statistics: The Basics not only serves as an excellent primer for beginners but it is also the perfectTrade Review"This book cuts to the very heart of the core principles of statistical inference and does so in a way that is accessible and easily digestible. I honestly wish a book like this one had existed when I was a student – I would have clutched it hard and never let it go!" -- Dr. Ruth Horry, Senior Lecturer in Psychology, Swansea University, UK"I see this book as a very useful resource, not only for those who have just started their journey at the university, but also for senior students to experience "aha!" moments while recapping the basics from a unique and nicely presented perspective. I am looking forward to recommending this book to my students as soon as it is available." -- Dr. Krzysztof Cipora, Lecturer in Mathematical Cognition, Loughborough University, UK"As a new graduate student, I was suddenly faced with academic papers presenting statistical methods. But with hardly any statistical understanding myself, I struggled to do this well. I longed for a book that I could easily refer back to. This is that book. The explanations are very accessible, the examples are relatable, and the book is concise. I thoroughly recommend it." -- Jennifer Read, Graduate Student in Education, University of Derby, UK"If you want to understand why we use statistics in psychology, this is the book for you!" -- Dawn Short, Ph.D. student in Psychology, Abertay University, UK"This is an accessible and helpful educational tool that students with a variety of backgrounds will enjoy. The author incorporates clear examples and is able to frame advanced concepts in a simple and straightforward way." -- Dr. Dawn Weatherford, Associate Professor of Psychology, Texas A&M University - San Antonio, USATable of Contents1. A (Very) Brief Introduction to Statistical Inference 2. Describing the Observed Data 3. Modeling the Observed Data 4. How Likely is the Observed Data? 5. Comparing Statistical Models 6. Introduction to the t-test 7. Bayesian Model Comparison 8. Recap and Next Steps
£18.99
Taylor & Francis Ltd HandsOn Data Science for Librarians
Book SynopsisLibrarians understand the need to store, use and analyze data related to their collection, patrons and institution, and there has been consistent interest over the last 10 years to improve data management, analysis, and visualization skills within the profession. However, librarians find it difficult to move from out-of-the-box proprietary software applications to the skills necessary to perform the range of data science actions in code. This book will focus on teaching R through relevant examples and skills that librarians need in their day-to-day lives that includes visualizations but goes much further to include web scraping, working with maps, creating interactive reports, machine learning, and others. While there's a place for theory, ethics, and statistical methods, librarians need a tool to help them acquire enough facility with R to utilize data science skills in their daily work, no matter what type of library they work at (academic, public or special). By walking through eTable of Contents1. Introduction 2. Using RStudio’s IDE 3. Tidying data with dplyr 4. Visualizing your project with ggplot2 5. Webscraping with rvest 6. Mapping with tmap 7. Textual Analysis with tidytext 8. Creating Dynamic Documents with rmarkdown 9. Creating a flexdashboard 10. Creating an interactive dashboard with shiny 11. Using tidymodels to Understand Machine Learning 12. Conclusion Appendix A. Dependencies Appendix B. Additional Skills
£54.99
CRC Press Bayesian Inference
Book SynopsisBayesian Inference: Theory, Methods, Computations provides a comprehensive coverage of the fundamentals of Bayesian inference from all important perspectives, namely theory, methods and computations.All theoretical results are presented as formal theorems, corollaries, lemmas etc., furnished with detailed proofs. The theoretical ideas are explained in simple and easily comprehensible forms, supplemented with several examples. A clear reasoning on the validity, usefulness, and pragmatic approach of the Bayesian methods is provided. A large number of examples and exercises, and solutions to all exercises, are provided to help students understand the concepts through ample practice. The book is primarily aimed at first or second semester master students, where parts of the book can also be used at Ph.D. level or by research community at large. The emphasis is on exact cases. However, to gain further insight into the core concepts, an entire chapter is dedicated to
£61.99
Taylor & Francis Ltd Telling Stories with Data
Book SynopsisThe book equips students with the end-to-end skills needed to do data science. That means gathering, cleaning, preparing, and sharing data, then using statistical models to analyse data, writing about the results of those models, drawing conclusions from them, and finally, using the cloud to put a model into production, all done in a reproducible way.At the moment, there are a lot of books that teach data science, but most of them assume that you already have the data. This book fills that gap by detailing how to go about gathering datasets, cleaning and preparing them, before analysing them. There are also a lot of books that teach statistical modelling, but few of them teach how to communicate the results of the models and how they help us learn about the world. Very few data science textbooks cover ethics, and most of those that do, have a token ethics chapter. Finally, reproducibility is not often emphasised in data science books. This book is based around a straight-forward workflow conducted in an ethical and reproducible way: gather data, prepare data, analyse data, and communicate those findings. This book will achieve the goals by working through extensive case studies in terms of gathering and preparing data, and integrating ethics throughout. It is specifically designed around teaching how to write about the data and models, so aspects such as writing are explicitly covered. And finally, the use of GitHub and the open-source statistical language R are built in throughout the book.Key Features: Extensive code examples. Ethics integrated throughout. Reproducibility integrated throughout. Focus on data gathering, messy data, and cleaning data. Extensive formative assessment throughout. Trade Review"This clean and fun book covers a wide range of topics on statistical communication, programming, and modeling in a way that should be a useful supplement to any statistics course or self-learning program. I absolutely love this book!"- Andrew Gelman, Columbia University"An excellent book. Communication and reproducibility are of increasing concern in statistics, and this book covers these topics and more in a practical, appealing, and truly unique way."- Daniela Witten, University of Washington"Many data science texts tell you how to perform perfunctory calculations. Instead, Telling Stories with Data tells you how to engage in the mindset and process of analysis. By arming students with the computational, statistical and philosophical skills needed to use data in sense-making and story-telling, this book stands out from the pack as uniquely actionable and empowering."- Emily Riederer, Capital One"This is not another statistics book. It is much better than that. It is a book about doing quantitative research, about scientific justification, about quality control, about communication and epistemic humility. It's a valuable supplement to any methods curriculum, and useful for self-learners as well."- Richard McElreath, Max Planck Institute for Evolutionary Anthropology"Telling Stories with Data is a thoughtful guide to using data to learn and affect positive change. The book includes each stage of the process and can serve as a long-lasting companion to many data scientists and future data story tellers."- Christopher Peters, Zapier“A clever career choice is to pick a field where your skills are complementary with a growing resource. In the coming decades, those who are adept in analysing data will flourish. That means crunching statistics and telling compelling stories. Rohan Alexander’s book will help you do both.”- Andrew Leigh, Member of the Australian Parliament and author of Randomistas: How Radical Researchers Are Changing Our World"Every data analyst has to tell stories with data, and yet traditional textbooks focus on statistical methods alone. Telling Stories with Data teaches the entire data science workflow, including data acquisition, communication, and reproducibility. I highly recommend this unique book!"- Kosuke Imai, Harvard University"This is an extraordinary, wonderful, book, full of wise advice for anyone starting in data science. Intermixing concepts and code means the ideas are immediately made concrete, and the emphasis on reproducible workflows brings a welcome dose of rigor to a rapidly developing field."- David Spiegelhalter, The University of CambridgeTable of Contents1. Telling stories with data 2. Drinking from a fire hose 3. Reproducible workflows Part 1. Foundations 4. Writing research 5. Static communication Part 2. Communication 6. Farm data 7. Gather data 8. Hunt data Part 3. Acquisition 9. Clean and prepare 10. Store and share Part 4. Preparation 11. Exploratory data analysis 12. Linear models 13. Generalized linear models 14. Causality from observational data 15. Multilevel regression with post-stratification 16. Text as data 17. Concluding remarks
£73.14
Taylor & Francis Ltd Safety Accidents in Risky Industries
Book SynopsisThis text introduces bad events (incidents and accidents) named as metaphors. The metaphors, called as safety animals, are named as black swan, gray rhino, gray swans, and invisible gorilla.The book analyzes incidents and accidents from the context of the safety management system in the risky industries including aviation, nuclear, chemical, oil, and petroleum. It further uses mathematical analysis of these events (through statistics and probabilities) and presents preventive and corrective measures in dealing with the same.It comprehensively covers important topics including real-time monitoring, reverse stress testing, change management, predictive maintenance, management system, contingency plans, human factors, behavioral safety, anticipatory failure determination, resilience engineering (RE), resilience management (RM), Swiss cheese model, and probability distribution.Aimed at professionals working in the fields of health and safety, quTable of Contents1. Philosophy of Science as Introduction. 2. The Black Swan events. 3. Analysis of the “Fat-tails” in Safety. 4. What to do with BSe in the Risky Industry?. 5. The Gray Rhino events. 6. Specifics of GRe in Risky Industry. 7. The Invisible Gorilla. 8. Other “Safety Animals”. 9. How to fight “Safety Animals”?. 10. Top Management and “Safety Animals”. 11. Final Words.
£147.25
CRC Press Class Field Theory and L Functions
Book Synopsis
£66.49
CRC Press Foundations of Quantitative Finance Book VI
Book SynopsisEvery finance professional wants and needs a competitive edge. A firm foundation in advanced mathematics can translate into dramatic advantages to professionals willing to obtain it. Many are notâand that is the competitive edge these books offer the astute reader.Published under the collective title of Foundations of Quantitative Finance, this set of ten books develops the advanced topics in mathematics that finance professionals need to advance their careers. These books expand the theory most do not learn in graduate finance programs, or in most financial mathematics undergraduate and graduate courses.As an investment executive and authoritative instructor, Robert R. Reitano presents the mathematical theories he encountered and used in nearly three decades in the financial services industry and two decades in academia where he taught in highly respected graduate programs.Readers should be quantitatively literate and familiar with the development
£71.24
Taylor & Francis Ltd Functional Data Analysis with R
Book SynopsisEmerging technologies generate data sets of increased size and complexity that require new or updated statistical inferential methods and scalable, reproducible software. These data sets often involve measurements of a continuous underlying process, and benefit from a functional data perspective. Functional Data Analysis with R presents many ideas for handling functional data including dimension reduction techniques, smoothing, functional regression, structured decompositions of curves, and clustering. The idea is for the reader to be able to immediately reproduce the results in the book, implement these methods, and potentially design new methods and software that may be inspired by these approaches.Features: Functional regression models receive a modern treatment that allows extensions to many practical scenarios and development of state-of-the-art software The connection between functional regression, penalized smoothing, and mixed effects models is used as the cornerstone for inference Multilevel, longitudinal, and structured functional data are discussed with emphasis on emerging functional data structures Methods for clustering functional data before and after smoothing are discussed Multiple new functional data sets with dense and sparse sampling designs from various application areas are presented, including the NHANES linked accelerometry and mortality data, COVID-19 mortality data, CD4 counts data and the CONTENT child growth study Step-by-step software implementations are included, along with a supplementary website (www.FunctionalDataAnalysis.com) featuring software, data, and tutorials More than 100 plots for visualization of functional data are presented Functional Data Analysis with R is primarily aimed at undergraduate, master's and PhD students, as well as data scientists and researchers working on functional data analysis. The book can be read at different levels and combines state-of-the-art software, methods, and inference. It can be used for self-learning, teaching, and research, and will particularly appeal to anyone who is interested in practical methods for hands-on, problem-forward functional data analysis. The reader should have some basic coding experience, but expertise in R is not required.
£76.99
Taylor & Francis Ltd Data Sketches Posters and Postcards
Book SynopsisTo celebrate Data Sketches'' 1-year anniversary, Nadieh Bremer and Shirley Wu have created an exclusive set of high-quality prints and postcards based on their bestselling dataviz book. The high-quality prints and postcards pack is also available in a discounted set including the book Data Sketches. The set ISBN is: 9781032303895.
£17.09
Taylor & Francis Making Sense of Statistics
Book SynopsisMaking Sense of Statistics, Eighth Edition, is the ideal introduction to the concepts of descriptive and inferential statistics for students undertaking their first research project. It presents each statistical concept in a series of short steps, then uses worked examples and exercises to enable students to apply their own learning.It focuses on presenting the why, as well as the how of statistical concepts, rather than computations and formulas. As such, it is suitable for students from all disciplines regardless of mathematical background. Only statistical techniques that are almost universally included in introductory statistics courses, and widely reported in journals, have been included. This conceptual book is useful for all study levels, from undergraduate to doctoral level across disciplines. Once students understand and feel comfortable with the statistics presented in this book, they should find it easy to master additional statistical concepts.Table of ContentsIntroduction: What is Research?; Part A: The Research Context 1. The Empirical Approach to Knowledge 2. Types of Empirical Research 3. Scales of Measurement 4. Descriptive, Correlational, and Inferential Statistics; Part B: Sampling 5. Introduction to Sampling 6. Variations on Random Sampling 7. Sample Size 8. Standard Error of Mean and Central Limit Theorem; Part C: Descriptive Statistics 9. Frequencies, Percentages, and Proportions 10. Shapes of Distributions 11. The Mean: An Average 12. Mean, Median, and Mode 13. Range and Interquartile Range 14. Standard Deviation 15. Z Score; Part D: Correlational Statistics 16. Correlation 17. Pearson r 18. Scattergram 19. Coefficient of Determination 20. Multiple Correlation; Part E: Inferential Statistics 21. Introduction to Null Hypothesis 22. Decisions About the Null Hypothesis 23. Limits of Significance Testing and Practical Implications; Part F: Means Comparison 24. Introduction to the t Test 25. Independent Samples t Test 26. Dependent Samples t Test 27. One Sample t Test 28. Reporting the Results of t Tests: Display of Outcomes 29. One-Way ANOVA 30. Two-Way ANOVA; Part G: Predictive Significance 31. Chi-Square 32. Effect Size 33. Simple and Multiple Linear Regressions; Appendix A. Computations Appendix B. Notes on Interpreting Pearson r and Linear Regression Appendix C. Table of Random Numbers
£118.75
CRC Press Exploring Complex Survey Data Analysis Using R
Book SynopsisSurveys are powerful tools for gathering information, uncovering insights, and facilitating decision-making. However, to ensure the accurate interpretation of results, they require specific analysis methods. In this book, readers embark on an in-depth journey into conducting complex survey analysis with the {srvyr} package and tidyverse family of functions from the R programming language. Intended for intermediate R users familiar with the basics of the tidyverse, this book gives readers a deeper understanding of applying appropriate survey analysis techniques using {srvyr}, {survey}, and other related packages. With practical walkthroughs featuring real-world datasets, such as the American National Election Studies and Residential Energy Consumption Survey, readers will develop the skills necessary to perform impactful survey analysis on survey data collected through a randomized sample design. Additionally, this book teaches readers how to interpret and communicate results of surv
£64.59
CRC Press Combinatorial Optimization Under Uncertainty
Book SynopsisThis book discusses the basic ideas, underlying principles, mathematical formulations, analysis and applications of the different combinatorial problems under uncertainty and attempts to provide solutions for the same. Uncertainty influences the behaviour of the market to a great extent. Global pandemics and calamities are other factors which affect and augment unpredictability in the market. The intent of this book is to develop mathematical structures for different aspects of allocation problems depicting real life scenarios. The novel methods which are incorporated in practical scenarios under uncertain circumstances include the STAR heuristic approach, Matrix geometric method, Ranking function and Pythagorean fuzzy numbers, to name a few. Distinct problems which are considered in this book under uncertainty include scheduling, cyclic bottleneck assignment problem, bilevel transportation problem, multi-index transportation problem, retrial queuing, uncertain matrix games, optimalTable of ContentsPreface. About the Editors. Chapter 1 Estimation of Uncertainties for Multiserver Queuing Systems with Bernoulli Feedback. Chapter 2 Optimality for Fuzzy Transportation Problem under Ranking Method. Chapter 3 Solution of Bilevel Linear Fractional Transportation Problem with Pythagorean Fuzzy Numbers. Chapter 4 Optimal Production Evaluation of Cotton in Different Soil and Water Conditions in Sundarban of West Bengal under Hesitant Interval Fuzzy Environment Using Projection Measures. Chapter 5 A Novel Approach for Feature Detection in Vector Graphics. Chapter 6 On Uncertain Matrix Games Involving Linguistic Pythagorean Fuzzy Sets. Chapter 7 Cyclic Surgery Scheduling using Variations of Cohort Intelligence. Chapter 8 Cone Method for Uncertain Multiobjective Optimization Problems with Minmax Robustness. Chapter 9 Solving Multi-Index Transportation Problem with Axial Constraints Having Impaired Flow. Chapter 10 STAR Heuristic Method: A Novel Approach and Its Comparative Analysis with CI Algorithm to Solve CBAP in Healthcare. Chapter 11 Development and Optimization of Quadratic Programming Problems with Intuitionistic Fuzzy Parameters. Index
£67.49
Taylor & Francis Ltd Computer Vision
Book SynopsisComputer vision has made enormous progress in recent years, and its applications are multifaceted and growing quickly, while many challenges still remain. This book brings together a range of leading researchers to examine a wide variety of research directions, challenges, and prospects for computer vision and its applications.This book highlights various core challenges as well as solutions by leading researchers in the field. It covers such important topics as data-driven AI, biometrics, digital forensics, healthcare, robotics, entertainment and XR, autonomous driving, sports analytics, and neuromorphic computing, covering both academic and industry R&D perspectives. Providing a mix of breadth and depth, this book will have an impact across the fields of computer vision, imaging, and AI.Computer Vision: Challenges, Trends, and Opportunities covers timely and important aspects of computer vision and its applications, highlighting the challenges ahead and p
£120.00
Taylor & Francis Ltd HandsOn Data Analysis in R for Finance
Book SynopsisThe subject of this textbook is to act as an introduction to data science / data analysis applied to finance, using R and its most recent and freely available extension libraries. The targeted academic level is undergrad students with a major in data science and/or finance and graduate students, and of course practitioners or professionals who need a desk reference. Assumes no prior knowledge of R The content has been tested in actual university classes Makes the reader proficient in advanced methods such as machine learning, time series analysis, principal component analysis and more Gives comprehensive and detailed explanations on how to use the most recent and free resources, such as financial and statistics libraries or open database on the internet Table of Contents1. Your Working Environment 2. Reading Data in R 3. Financial Data 4. Introduction to R 5. Functions 6. Data Transformation 7. Merging Data Sets 8. Graphing Using Ggplot 9. Returns and Returns-based Statistics 10. Portfolios 11. Modeling Returns and Simulations 12. Linear and Polynomial Regression 13. Fixed Income 14. Principal Component Analysis 15. Options 16. Value at Risk 17. Time Series Analysis 18. Machine Learning 19. Presenting the Results of Your Analyses 20. Appendix: Main Packages Seen in this Book
£73.14
Taylor & Francis Ltd Digital Image Processing with C
Book SynopsisDigital Image Processing with C++: Implementing Reference Algorithms with the CImg Library presents the theory of digital image processing and implementations of algorithms using a dedicated library. Processing a digital image means transforming its content (denoising, stylizing, etc.), or extracting information to solve a given problem (object recognition, measurement, motion estimation, etc.). This book presents the mathematical theories underlying digital image processing, as well as their practical implementation through examples of algorithms implemented in the C++ language using the free and easy-to-use CImg library.Chapters cover the field of digital image processing in a broad way and propose practical and functional implementations of each method theoretically described. The main topics covered include filtering in spatial and frequency domains, mathematical morphology, feature extraction and applications to segmentation, motion estimation, multispecTable of ContentsI INTRODUCTION TO Clmg1. Introduction. 2. Getting Started With the CImg Library. 2.1 Objective: subdivide an image into blocks. 2.2 Setup and first program. 2.3 Computing the variations. 2.4 Computing the block decomposition. 2.5 Rendering of the decomposition. 2.6 Interactive visualization. 2.7 Final source code II IMAGE PROCESSING USING CImg3. Point Processing Transformations. 3.1 Image operations. 3.2 Histogram operations. 4. Mathematical Morphology. 4.1 Binary images. 4.2 Gray-level images. 4.3 Some applications. 5. Filtering. 5.1 Spatial filtering. 5.2 Recursive filtering. 5.3 Frequency filtering. 5.4 Diffusion filtering. 6. Feature Extraction. 6.1 Points of interest. 6.2 Hough transform. 6.3 Texture features. 7. Segmentation. 7.1 Edge-based approaches. 7.2 Region-based approaches. 8. Motion Estimation. 8.1 Optical flow: dense motion estimation. 8.2 Sparse estimation. 9. Multispectral Approaches. 9.1 Dimension reduction. 9.2 Color imaging. 10. 3D Visualisation. 10.1 Structuring of 3D mesh objects. 10.2 3D plot of a function z = f (x;y). 10.3 Creating complex 3D objects. 10.4 Visualization of a cardiac segmentation in MRI. 11. And So Many Other Things. 11.1 Compression by transform (JPEG). 11.2 Tomographic reconstruction. 11.3 Stereovision. 11.4 Interactive deformation using RBF. List of CImg Codes.References.Index.
£37.99
Taylor & Francis Ltd R for Quantitative Chemistry
Book SynopsisR for Quantitative Chemistry is an exploration of how the R language can be applied to a wide variety of problems in what is typically termed "Quantitative Chemistry" or sometimes "Analytical Chemistry". This book will be based upon, in large part, actual experimental data.Table of Contents1. Intro to R 2. Data and Statistics 3. Beer’s Law and Linear Regression 4. Solving Equilibrium 5. Titrations 6. Binding Curves 7. Electrochemistry 8. Fourier Transform and Spectroscopy 9. R Kinetic Analysis 10. Reports in R Markdown
£51.29
Taylor & Francis Ltd Spreadsheet Problem Solving and Programming for
Book SynopsisSpreadsheet Problem Solving and Programming for Engineers and Scientists provides a comprehensive resource essential to a full understanding of modern spreadsheet skills needed for engineering and scientific computations.Beginning with the basics of spreadsheets and programming, this book builds on the authors' decades of experience teaching spreadsheets and programming to both university students and professional engineers and scientists. Following on from this, it covers engineering economics, key numerical methods, and applied statistics. Finally, this book details the Visual Basic for Applications (VBA) programming system that accompanies Excel.With each chapter including examples and a set of exercises, this book is an ideal companion for all engineering courses and also for self-study. Based on the latest version of Excel (Microsoft Excel for Microsoft 365), it is also compatible with earlier versions of Excel dating back to Version 2013. Including numerTable of ContentsChapter 1 Spreadsheet Basics Chapter 2 Charts and GraphsChapter 3 Engineering and Scientific FormulasChapter 4 Table-based CalculationsChapter 5 Case Studies and TargetingChapter 6 Financial CalculationsChapter 7 Numerical MethodsChapter 8 Applied StatisticsChapter 9 Introduction to VBA and MacrosChapter 10 User-defined FunctionsChapter 11 VBA ProgrammingChapter 12 User InterfacesAppendix A: Matrix Algebra ReviewAppendix B: Shortcut Keys and Key Combinations
£87.39
Taylor & Francis Ltd Machine Learning Toolbox for Social Scientists
Book SynopsisMachine Learning Toolbox for Social Scientists covers predictive methods with complementary statistical tools that make it mostly self-contained. The inferential statistics is the traditional framework for most data analytics courses in social science and business fields, especially in Economics and Finance. The new organization that this book offers goes beyond standard machine learning code applications, providing intuitive backgrounds for new predictive methods that social science and business students can follow. The book also adds many other modern statistical tools complementary to predictive methods that cannot be easily found in econometrics textbooks: nonparametric methods, data exploration with predictive models, penalized regressions, model selection with sparsity, dimension reduction methods, nonparametric time-series predictions, graphical network analysis, algorithmic optimization methods, classification with imbalanced data, and many others. This book is targTable of Contents1. How We Define Machine Learning 2. Preliminaries Part 1. Formal Look at Prediction 3. Bias-Variance Tradeoff 4. Overfitting Part 2. Nonparametric Estimations 5. Parametric Estimations 6. Nonparametric Estimations - Basics 7. Smoothing 8. Nonparametric Classifier - kNN Part 3. Self-learning 9. Hyperparameter Tuning 10. Tuning in Classification 11. Classification Example Part 4. Tree-based Models 12. CART 13. Ensemble Learning 14. Ensemble Applications Part 5. SVM & Neural Networks 15. Support Vector Machines 16. Artificial Neural Networks Part 6. Penalized Regressions 17. Ridge 18. Lasso 19. Adaptive Lasso 20. Sparsity Part 7. Time Series Forecasting 21. ARIMA models 22. Grid Search for Arima 23. Time Series Embedding 24. Random Forest with Times Series 25. Recurrent Neural Networks Part 8. Dimension Reduction Methods 26. Eigenvectors and eigenvalues 27. Singular Value Decomposition 28. Rank r approximations 29. Moore-Penrose Inverse 30. Principle Component Analysis 31. Factor Analysis Part 9. Network Analysis 32. Fundamentals 33. Regularized Covariance Matrix Part 10. R Labs 34. R Lab 1 Basics 35. R Lab 2 Basics II 36. Simulations in R 37. Algorithmic Optimization 38. Imbalanced Data
£69.29
Taylor & Francis Ltd Lessons in Play
Book SynopsisThis second edition of Lessons in Play reorganizes the presentation of the popular original text in combinatorial game theory to make it even more widely accessible. Starting with a focus on the essential concepts and applications, it then moves on to more technical material. Still written in a textbook style with supporting evidence and proofs, the authors add many more exercises and examples and implement a two-step approach for some aspects of the material involving an initial introduction, examples, and basic results to be followed later by more detail and abstract results.Features Employs a widely accessible style to the explanation of combinatorial game theory Contains multiple case studies Expands further directions and applications of the field IncluTrade Review"The wisdom and joy outshining from this 2nd edition, beat even the original. The helpful preludes for student and instructor, prefacing each chapter, have elevated subtly in additional reader-friendliness; new subsections and a new case study were added. An interesting new Chapter 10 trades complex yet complete computation of a game’s strategy, with a simplified slightly approximate winning strategy. The last chapter, which awards the reader with a flavor of cutting edge research, was updated with a section on scoring games. The book is a must for novice and expert alike." —Aviezri Fraenkel, Weizmann Institute of Science, Israel "In this second edition of Lessons in Play, the authors have corrected errors, updated the bibliography, and added a new chapter on trimming game trees. Like the first edition, this new edition is beautifully typeset and illustrated." —Brian Borchers, Editor, MAA Reviews In this second edition of Lessons in Play: An Introduction to Combinatorial Game Theory, authors Albert , Nowakowski, and White provide a reorganized text presenting a variety of two-player finite games, discussed in theory as well as application. The theoretical material is presented in a clear and concise theorem/proof format and includes problems and exercises to aid readers’ understanding. Solutions are provided at the end of the book. Multiple examples from actual games are provided throughout, including Boxcars, Clobber, Cutthroat, Dots and Boxes, Hackenbush, and Toppling Dominoes. Throughout the text, the authors also provide in-depth case studies on specific games. A unique feature of this book is that each chapter begins by presenting a series of “prep problems” with notes to the instructor so students can preview the material prior to reading the chapter. Overall, this book is an excellent beginning read for anyone interested in learning about combinatorial games, assuming at least some background in abstract algebra. —S. L. Sullivan, Catawba College Praise for the previous edition This is an excellent introductory book to beginning game theory, written in an easily understandable manner yet advanced enough not to be considered trivial.—Books Online, July 2007 The first book to present combinatorial game theory in the form of a textbook suitable for students at the advanced undergraduate level … The authors state and prove theorems in a rigorous fashion [and] the presentation is enlivened with many concrete examples … an outstanding textbook … It will also be of interest to more advanced readers who want an introduction to combinatorial game theory.—Brian Borchers, June 2007 The theory is accessible to any student who has a smattering of general algebra and discrete math. Generally, a third year college student, but any good high school student should be able to follow the development with a little help.—Sir Read a Lot, May 2007 Lessons in Play is an enticing introduction to the wonderful world of combinatorial games. Using a rich collection of cleverly captivating examples and problems, the authors lead the reader through the basic concepts and on to several innovative extensions. I highly recommend this book.—Elwyn R. Berlekamp A neat machine, converting novices into enthusiastic experts in modern combinatorial game theory.—Aviezri Fraenkel Combinatorial games are intriguing, challenging, and often counter-intuitive, and are rapidly being recognized as an important mathematical discipline. Now that we have the attractive and friendly text Lessons in Play in hand, we can look forward to the appearance of many popular upper-division undergraduate courses, which encourage instructors to learn alongside their students.—Richard K. Guy … If you have Winning Ways, you must have this book.—Andy Liu "The wisdom and joy outshining from this 2nd edition, beat even the original. The helpful preludes for student and instructor, prefacing each chapter, have elevated subtly in additional reader-friendliness; new subsections and a new case study were added. An interesting new Chapter 10 trades complex yet complete computation of a game’s strategy, with a simplified slightly approximate winning strategy. The last chapter, which awards the reader with a flavor of cutting edge research, was updated with a section on scoring games. The book is a must for novice and expert alike." —Aviezri Fraenkel, Weizmann Institute of Science, Israel "In this second edition of Lessons in Play, the authors have corrected errors, updated the bibliography, and added a new chapter on trimming game trees. Like the first edition, this new edition is beautifully typeset and illustrated." —Brian Borchers, Editor, MAA Reviews In this second edition of Lessons in Play: An Introduction to Combinatorial Game Theory, authors Albert , Nowakowski, and White provide a reorganized text presenting a variety of two-player finite games, discussed in theory as well as application. The theoretical material is presented in a clear and concise theorem/proof format and includes problems and exercises to aid readers’ understanding. Solutions are provided at the end of the book. Multiple examples from actual games are provided throughout, including Boxcars, Clobber, Cutthroat, Dots and Boxes, Hackenbush, and Toppling Dominoes. Throughout the text, the authors also provide in-depth case studies on specific games. A unique feature of this book is that each chapter begins by presenting a series of “prep problems” with notes to the instructor so students can preview the material prior to reading the chapter. Overall, this book is an excellent beginning read for anyone interested in learning about combinatorial games, assuming at least some background in abstract algebra. —S. L. Sullivan, Catawba College Praise for the previous edition This is an excellent introductory book to beginning game theory, written in an easily understandable manner yet advanced enough not to be considered trivial.—Books Online, July 2007 The first book to present combinatorial game theory in the form of a textbook suitable for students at the advanced undergraduate level … The authors state and prove theorems in a rigorous fashion [and] the presentation is enlivened with many concrete examples … an outstanding textbook … It will also be of interest to more advanced readers who want an introduction to combinatorial game theory.—Brian Borchers, June 2007 The theory is accessible to any student who has a smattering of general algebra and discrete math. Generally, a third year college student, but any good high school student should be able to follow the development with a little help.—Sir Read a Lot, May 2007 Lessons in Play is an enticing introduction to the wonderful world of combinatorial games. Using a rich collection of cleverly captivating examples and problems, the authors lead the reader through the basic concepts and on to several innovative extensions. I highly recommend this book.—Elwyn R. Berlekamp A neat machine, converting novices into enthusiastic experts in modern combinatorial game theory.—Aviezri Fraenkel Combinatorial games are intriguing, challenging, and often counter-intuitive, and are rapidly being recognized as an important mathematical discipline. Now that we have the attractive and friendly text Lessons in Play in hand, we can look forward to the appearance of many popular upper-division undergraduate courses, which encourage instructors to learn alongside their students.—Richard K. Guy … If you have Winning Ways, you must have this book.—Andy Liu Table of ContentsCombinatorial Games 0.1 Basic Terminology Problems 1 Basic Techniques 1.1 Greedy 1.2 Symmetry 1.3 Parity 1.4 Give Them Enough Rope! 1.5 Strategy Stealing 1.6 Change the Game! 1.7 Case Study: Long Chains in Dots & Boxes Problems 2 Outcome Classes 2.1 Outcome Functions 2.2 Game Positions and Options 2.3 Impartial Games: Minding Your Ps and Ns 2.4 Case Study: Roll The Lawn 2.5 Case Study: Timber 2.6 Case Study: Partizan Endnim Problems 3 Motivational Interlude: Sums of Games 3.1 Sums 3.2 Comparisons 3.3 Equality and Identity 3.4 Case Study: Domineering Rectangles Problems 4 The Algebra of Games 4.1 The Fundamental Definitions 4.2 Games Form a Group with a Partial Order 4.3 Canonical Form 4.4 Case Study: Cricket Pitch 4.5 Incentives Problems 5 Values of Games 5.1 Numbers 5.2 Case Study: Shove 5.3 Stops 5.4 A Few All-Smalls: Up, Down, and Stars 5.5 Switches 5.6 Case Study: Elephants & Rhinos 5.7 Tiny and Miny 5.8 Toppling Dominoes 5.9 Proofs of Equivalence of Games and Numbers Problems 6 Structure 6.1 Games Born by Day 2 6.2 Extremal Games Born By Day n 6.3 More About Numbers 6.4 The Distributive Lattice of Games Born by Day n 6.5 Group Structure Problems 7 Impartial Games 7.1 A Star-Studded Game 7.2 The Analysis of Nim 7.3 Adding Stars 7.4 A More Succinct Notation 7.5 Taking-and-Breaking Games 7.6 Subtraction Games 7.7 Keypad Games Problems 8 Hot Games 8.1 Comparing Games and Numbers 8.2 Coping with Confusion 8.3 Cooling Things Down 8.4 Strategies for Playing Hot Games 8.5 Norton Products Problems 9 All-Small Games 9.1 Cast of Characters 9.2 Motivation: The Scale of Ups 9.3 Equivalence Under 9.4 Atomic Weight 9.5 All-Small Shove 9.6 More Toppling Dominoes 9.7 Clobber Problems 10 Trimming Game Trees 10.1 Introduction 10.2 Reduced Canonical Form 10.3 Hereditary-Transitive Games 10.4 Ordinal Sum 10.5 Stirling-Shave 10.6 Even More Toppling Dominoes Problems Further Directions 1 Transfinite Games 2 Algorithms and Complexity 3 Loopy Games 4 Kos: Repeated Local Positions 5 Top-Down Thermography 6 Enriched Environments 7 Idempotents 8 Mis`ere Play 9 Scoring Games A Top-Down Induction A.1 Top-Down Induction A.2 Examples
£43.99
Taylor & Francis Ltd Sports Math
Book SynopsisCan you really keep your eye on the ball? How is massive data collection changing sports?Sports science courses are growing in popularity. The author's course at Roanoke College is a mix of physics, physiology, mathematics, and statistics. Many students of both genders find it exciting to think about sports. Sports problems are easy to create and state, even for students who do not live sports 24/7. Sports are part of their culture and knowledge base, and the opportunity to be an expert on some area of sports is invigorating. This should be the primary reason for the growth of mathematics of sports courses: the topic provides intrinsic motivation for students to do their best work.From the Author:The topics covered in Sports Science and Sports Analytics courses vary widely. To use a golfing analogy, writing a book like this is like hitting a drive at a driving range; there are many Trade ReviewThe book is written at a level that is accessible to a large audience. It contains a small number of applications that make use of calculus; otherwise, only a high school level mathematics background is required. Furthermore, one can easily skip over those sections that require calculus and still have plenty of accessible material to read.Sports Math is well written and easy to read. The book should appeal to anyone interested in the quantitative aspects of athletics. Each chapter of the books ends with a fairly large number of exercises and also pointers to further reading. Thus, the book could be used not only as a textbook for a course but also as a nice resource for student projects.~Mathematical Reviews, 2017Minton presents a textbook based on the current status of a sport science course that has evolved since he began teaching it in 1988. He offers a sample of topics that he knows something about and finds interesting, and hopes that instructors and students will find the book useful. His topics are projectile motion, rotational motion, sports illusions, collisions, ratings systems, voting systems, saber- and other metrics, randomness in sports, sports strategies, and big data and beyond.~ProtoView, 2017 This work discusses how mathematics is used to analyze popular American sports like football, baseball, and basketball. Minton (mathematics, Roanoke College) has based this book on several of his undergraduate courses. The book covers two major aspects: the physics involved in sports (e.g., the motion of a ball) and the statistics used to make probabilistic ratings of performance and success. The beginning chapters consider topics from mechanics, such as “Projectile Motion,” “Rotational Motion,” and “Collisions.” The rest of the text is devoted to statistics used in sports ratings and analysis, with many examples from specific games played in the big leagues or by major colleges. The material covered is selective and quirky; the level of analytical mathematics and statistics ranges from simple to advanced, including calculus, matrixes, and game theory. Each chapter has solved examples and end-of-chapter questions, problems, and suggestions for projects. There are pictures and graphs interspersed throughout the text. The book is not suitable as a standard text in any conventional course—it will best serve as a supplement.--N. Sadanand, Central Connecticut State University 2018The book is written at a level that is accessible to a large audience. It contains a small number of applications that make use of calculus; otherwise, only a high school level mathematics background is required. Furthermore, one can easily skip over those sections that require calculus and still have plenty of accessible material to read.Sports Math is well written and easy to read. The book should appeal to anyone interested in the quantitative aspects of athletics. Each chapter of the books ends with a fairly large number of exercises and also pointers to further reading. Thus, the book could be used not only as a textbook for a course but also as a nice resource for student projects.~Mathematical Reviews, 2017Minton presents a textbook based on the current status of a sport science course that has evolved since he began teaching it in 1988. He offers a sample of topics that he knows something about and finds interesting, and hopes that instructors and students will find the book useful. His topics are projectile motion, rotational motion, sports illusions, collisions, ratings systems, voting systems, saber- and other metrics, randomness in sports, sports strategies, and big data and beyond.~ProtoView, 2017This work discusses how mathematics is used to analyze popular American sports like football, baseball, and basketball. Minton (mathematics, Roanoke College) has based this book on several of his undergraduate courses. The book covers two major aspects: the physics involved in sports (e.g., the motion of a ball) and the statistics used to make probabilistic ratings of performance and success. The beginning chapters consider topics from mechanics, such as “Projectile Motion,” “Rotational Motion,” and “Collisions.” The rest of the text is devoted to statistics used in sports ratings and analysis, with many examples from specific games played in the big leagues or by major colleges. The material covered is selective and quirky; the level of analytical mathematics and statistics ranges from simple to advanced, including calculus, matrixes, and game theory. Each chapter has solved examples and end-of-chapter questions, problems, and suggestions for projects. There are pictures and graphs interspersed throughout the text. The book is not suitable as a standard text in any conventional course—it will best serve as a supplement.--N. Sadanand, Central Connecticut State University 2018Table of ContentsProjectile Motion. Rotational Motion. Sports Illusions. Collisions. Rating Systems. Voting Systems. Saber- and Other Metrics. Randomness in Sports. In-Game Strategies. Predictive Analytics.
£43.99
Taylor & Francis Ltd Polya Urn Models
Book SynopsisIncorporating a collection of recent results, Pólya Urn Models deals with discrete probability through the modern and evolving urn theory and its numerous applications. The book first substantiates the realization of distributions with urn arguments and introduces several modern tools, including exchangeability and stochastic processes via urns. It reviews classical probability problems and presents dichromatic Pólya urns as a basic discrete structure growing in discrete time. The author then embeds the discrete Pólya urn scheme in Poisson processes to achieve an equivalent view in continuous time, provides heuristical arguments to connect the Pólya process to the discrete urn scheme, and explores extensions and generalizations. He also discusses how functional equations for moment generating functions can be obtained and solved. The final chapters cover applications of urns to computer science and bioscience. Examining how urns can help conceptualize discrete probability Trade Review"Pólya Urn Models describes the established theory and details numerous new developments since the seminal 1977 text Urn Models and Their Application: An Approach to Modern Discrete Probability Theory by Johnson and Kotz. … the second half of Mahmoud’s book largely covers new advances since Johnson and Kotz’s book … This latter material makes the current text unique. … The end-of-chapter exercises (of greatly varying difficulty) have excellent in-depth solutions at the end of the text. … The book is exceptionally well written, concise, and compelling. The author obviously commands this material, and his enthusiasm is unmistakable."—Matthew Bognar, Journal of the American Statistical Association, September 2014, Vol. 109Table of ContentsUrn Models and Rudiments of Discrete Probability. Some Classical Urn Problems.Pólya Urn Models.Poissonization. The Depoissonization Heuristic. Urn Schemes with Random Replacement.Analytic Urns.Applications of Pólya Urns in Informatics.Urn Schemes in Bioscience.Urns Evolving by Multiple Drawing. Answers to Exercises. Notation. Bibliographic Notes. Bibliography. Index.
£43.99
Taylor & Francis Ltd Statistical Inference Based on the likelihood
Book SynopsisThe Likelihood plays a key role in both introducing general notions of statistical theory, and in developing specific methods. This book introduces likelihood-based statistical theory and related methods from a classical viewpoint, and demonstrates how the main body of currently used statistical techniques can be generated from a few key concepts, in particular the likelihood.Focusing on those methods, which have both a solid theoretical background and practical relevance, the author gives formal justification of the methods used and provides numerical examples with real data.Trade Review"...this book reads well, and it is a welcome addition to the literature. I recommend its use as a text for an introductory graduate level course."-JASA"...provides numerical answers with real data."-L'Enseignment Mathematique"From the preface: 'the aim is to show how the main body of currently used statistical techniques can be generated from a few key concepts, in particular the likelihood'. The author achieves this aim extremely well."-Publication of the International Statistical InstituteTable of ContentsStatistical Inference Based on the likelihood
£43.99
Taylor & Francis Ltd The Polls Werent Wrong
Book SynopsisInterpreting poll data as a prediction of election outcomes is a practice as old as the field, rooted in a fundamental misunderstanding of what poll data means.By first understanding how polls work at a fundamental level, this book gives readers the ability to discern flaws in the current methods. Then, through specific political examples from both the United States and the United Kingdom, it is shown how polls famously derided as wrong were, in fact, accurate. While polls are not always accurate, the reasons we can and can't (rightly) call them wrong are explained in this book.This book will equip readers with the tools to navigate the mismatch of expectations. It is not intended to replace more technical applications of statistics but is accessible to anyone interested in learning more about how poll data should be understood, compared to how it's currently misunderstood.
£36.99
Taylor & Francis Ltd Applied Machine Learning Using mlr3 in R
Book Synopsismlr3 is an award-winning ecosystem of R packages that have been developed to enable state-of-the-art machine learning capabilities in R. Applied Machine Learning Using mlr3 in R gives an overview of flexible and robust machine learning methods, with an emphasis on how to implement them using mlr3 in R. It covers various key topics, including basic machine learning tasks, such as building and evaluating a predictive model; hyperparameter tuning of machine learning approaches to obtain peak performance; building machine learning pipelines that perform complex operations such as pre-processing followed by modelling followed by aggregation of predictions; and extending the mlr3 ecosystem with custom learners, measures, or pipeline components.Features: In-depth coverage of the mlr3 ecosystem for users and developers Explanation and illustration of basic and advanced machine learning concepts Ready to use code samples that can be adapted by the useTable of Contents1. Introduction and Overview. 2. Data and Basic Modeling. 3. Evaluation and Benchmarking. 4. Hyperparameter Optimization. 5. Advanced Tuning Methods and Black Box Optimization. 6. Feature Selection. 7. Sequential Pipelines. 8. Non-sequential Pipelines and Tuning. 9. Preprocessing. 10. Advanced Technical Aspects of mlr3 .11. Model Interpretation and Explanation. 12. Model Interpretation. 13. Beyond Regression and Classification. 14. Algorithmic Fairness.
£61.99
Taylor & Francis An Introduction to Applied Statistics
Book SynopsisAn Introduction to Applied Statistics offers a comprehensive and accessible foundation in applied statistics, empowering students with the essential concepts and practical skills necessary for data-driven decision-making in today's world. Thoroughly covering key topicsâincluding data management, probability fundamentals, data screening, descriptive statistics, and a broad spectrum of inferential analysis techniquesâthis indispensable guide demystifies statistical concepts and equips students to confidently apply statistical analysis in real-world contexts.With a systematic, beginner-friendly approach, the author assumes no prior knowledge, making complex statistical foundations accessible to students from a variety of disciplines. Concise, digestible chapters build statistical competencies within a practical, evidence-based framework, minimizing technical jargon to facilitate comprehension. Now in its latest edition, the book is fully updated with SPSS v29.0 instructio
£70.29
Taylor & Francis Ltd Statistical Inference
Book SynopsisThis classic textbook builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and natural extensions, and consequences, of previous concepts. It covers all topics from a standard inference course including: distributions, random variables, data reduction, point estimation, hypothesis testing, and interval estimation.Features The classic graduate-level textbook on statistical inference Develops elements of statistical theory from first principles of probability Written in a lucid style accessible to anyone with some background in calculus Covers all key topics of a standard course in inference Hundreds of examples throughout to aid understanding Each chapter includes an extensive set of graduated exercises
£71.99
CRC Press Quantitative Social Science Research in Practice
Book SynopsisQuantitative Social Science Research in Practice: Generating Novel and Parsimonious Explanatory Models for Social Sciences examines quantitative Behavioral Science Research (BSR) by focusing on four key areas:Developing Novel, Parsimonious, and Actionable Causal Models: Researchers often face challenges in creating new, parsimonious causal models supported by empirical evaluation. A promising approach involves using meta-analytic reviews and more recent studies to identify relevant constructs and hypotheses that would constitutethe new causal model.Exploring the Scope of Context for a Novel Causal Model: The relevance of causal models may vary based on context, such as national or organizational culture, economic and political situations, and feasibility constraints. Behavioural science researchers have struggled to balance rigor and relevance, as theories effective in one context may not be valid in another. This book presents an approa
£58.89
CRC Press Statistical Inference via Data Science
Book SynopsisStatistical Inference via Data Science: A ModernDive into R and the Tidyverse, Second Edition offers a comprehensive guide to learning statistical inference with data science tools widely used in industry, academia, and government. The first part of this book introduces the tidyverse suite of R packages, including ggplot2 for data visualization and dplyr for data wrangling. The second part introduces data modeling via simple and multiple linear regression. The third part presents statistical inference using simulation-based methods within a general framework implemented in R via the infer package, a suitable complement to the tidyverse. By working with these methods, readers can implement effective exploratory data analyses, conduct statistical modeling with data, and carry out statistical inference via confidence intervals and hypothesis testing. All these tasks are performed strongly emphasizing data visualization.Key Features in
£61.74
CRC Press Financial Data Analytics with R
Book SynopsisFinancial Data Analysis with R: Monte-Carlo Validation is a comprehensive exploration of statistical methodologies and their applications in finance. Readers are taken on a journey in each chapter through practical explanations and examples, enabling them to develop a solid foundation of these methods in R and their applications in finance.This book serves as an indispensable resource for finance professionals, analysts, and enthusiasts seeking to harness the power of data-driven decision-making.The book goes beyond just teaching statistical methods in R and incorporates a unique section of informative Monte-Carlo simulations. These Monte-Carlo simulations are uniquely designed to showcase the reader the potential consequences and misleading conclusions that can arise when fundamental model assumptions are violated. Through step-by-step tutorials and realworld cases, readers will learn how and why model assumptions are important to follow.With a focus on
£58.89
CRC Press Causal Analysis for Climate Study
Book SynopsisThis book offers the theory of causal analysis and its applications. The authors have developed this book in relation its applications to four climatological phenomena, to prove the theory of causal analysis in time sequential data analysis.Local Causal Test and the Partial Causal Test are used to study the theory of causal analysis. These are then applied to understand the climate effects of the eruption at Mt. Pinatubo, effect of the El Nino and Southern Oscillation (ENSO), and North Atlantic Oscillation (NAO). The reader learns about Test(S), a statistical test used to determine if the statistical properties of the data, such as mean and variance, remain constant over time within a local window. The authors also use the Stationary Test and Causality Test in time series to explore relationships within a localized subset of data as witnessed from the climate phenomenon. The book looks at the eruption at Mt. Pinatubo, ENSO and NAO and applies causal theory to study the result
£48.99
Cambridge University Press Probability Theory and Statistical Inference
a huge range and FREE tracked UK delivery on ALL orders.
£94.99
Cambridge University Press Fourier Analysis 85 London Mathematical Society Student Texts Series Number 85
Book SynopsisFourier analysis aims to decompose functions into a superposition of simple trigonometric functions, whose special features can be exploited to isolate specific components into manageable clusters before reassembling the pieces. This two-volume text presents a largely self-contained treatment, comprising not just the major theoretical aspects (Part I) but also exploring links to other areas of mathematics and applications to science and technology (Part II). Following the historical and conceptual genesis, this book (Part I) provides overviews of basic measure theory and functional analysis, with added insight into complex analysis and the theory of distributions. The material is intended for both beginning and advanced graduate students with a thorough knowledge of advanced calculus and linear algebra. Historical notes are provided and topics are illustrated at every stage by examples and exercises, with separate hints and solutions, thus making the exposition useful both as a course Trade Review'[Fourier Analysis: Volume l - Theory is] fabulous … Constantin structures his exercise sets beautifully, I think: they are abundant and long, covering a spectrum of levels of difficulty; each set is followed immediately by a section of hints (in one-one correspondence); finally the hints sections are followed by very detailed and well-written solutions (also bijectively). Can there be any clearer homage to the maxim that to learn mathematics one has to get one's hands really dirty? To boot, attention to detail is ubiquitous: it's everywhere in Constantin's presentation of proofs and arguments, as well as examples, all throughout the narrative itself. The entire presentation is very much to the point and the student who works through this book will come out knowing some real mathematics very well.' Michael Berg, MAA ReviewsTable of Contents1. Introduction; 2. The Lebesgue measure and integral; 3. Elements of functional analysis; 4. Convergence results for Fourier series; 5. Fourier transforms; 6. Multi-dimensional Fourier analysis; 7. A glance at some advanced topics; Appendix: historical notes; References; Index.
£42.74
Cambridge University Press Teaching Statistics
Book SynopsisStatistics has developed in parallel with the advances of technological and social change. Informed by the work of the Cambridge Mathematics team, this book outlines a new pedagogical approach to teaching statistics. It frames the interconnectedness of the subject around the experiences that students should have, rather than the specific techniques required. The book provides numerous examples and suggestions that teachers can incorporate in the classroom to help improve the way students understand statistics.Table of ContentsIntroduction; Part 1 A vision for statistics in schools: 1. What should all students understand and why is it important?; 2. The statistical cycle; 3. Exploratory data analysis; 4. Simulation; 5. Sampling and variation; 6. Signals and noise; 7. Informal inference; 8. Technology in the classroom; Part 2 Activities; 9. Activity for Chapter 2; 10. Activity for Chapter 3; 11. Activities for Chapter 4; 12. Activities for Chapter 5; 13. Activity for Chapter 6; 14. Activity for Chapter 7; Appendix 1; Appendix 2; Index.
£24.75
Cambridge University Press Stable Levy Processes via LampertiType
Book SynopsisStable Lévy processes lie at the intersection of Lévy processes and self-similar Markov processes. Processes in the latter class enjoy a Lamperti-type representation as the space-time path transformation of so-called Markov additive processes (MAPs). This completely new mathematical treatment takes advantage of the fact that the underlying MAP for stable processes can be explicitly described in one dimension and semi-explicitly described in higher dimensions, and uses this approach to catalogue a large number of explicit results describing the path fluctuations of stable Lévy processes in one and higher dimensions. Written for graduate students and researchers in the field, this book systemically establishes many classical results as well as presenting many recent results appearing in the last decade, including previously unpublished material. Topics explored include first hitting laws for a variety of sets, path conditionings, law-preserving path transformations, the distribution of eTrade Review'This treatise takes readers on a superb journey through the fascinating worlds of stable Lévy processes and of a rich variety of further naturally related random processes. Andreas Kyprianou and Juan Carlos Pardo masterfully deploy an arsenal of techniques, which are already interesting on their own right, to reveal many classical or more recent high level results on the distributions of functionals and on the path behaviours stable processes. It is indeed remarkable that their methods lead to so many explicit formulas, some amazingly simple, some more complex. The authors should be praised for making accessible as a coherent whole a vast literature that has been developed over several decades, including the latest developments.' Jean Bertoin, University of ZurichTable of Contents1. Stable distributions; 2. Lévy processes; 3. Stable processes; 4. Hypergeometric Lévy processes; 5. Positive self-similar Markov processes; 6. Spatial fluctuations in one dimension; 7. Doney–Kuznetsov factorisation and the maximum; 8. Asymptotic behaviour for stable processes; 9. Envelopes of positive self-similar Markov processes; 10. Asymptotic behaviour for path transformations; 11. Markov additive and self-similar Markov processes; 12. Stable processes as self-similar Markov processes; 13. Radial reflection and the deep factorisation; 14. Spatial fluctuations and the unit sphere; 15. Applications of radial excursion theory; 16. Windings and up-crossings of stable processes; Appendix.
£52.19
Cambridge University Press Stochastic Modelling of ReactionDiffusion Processes
a huge range and FREE tracked UK delivery on ALL orders.
£104.50
Cambridge University Press Applied Mixed Model Analysis
Book SynopsisThis book explains all aspects of mixed model analysis without mathematical jargon, so that non-statisticians can understand the basic principles, analyze their own data, and interpret the results with confidence. Worked examples are analyzed with STATA, and all datasets are available for download, equipping readers to replicate the methods.Table of Contents1. Introduction; 2. Basic principles of mixed model analysis; 3. What is gained by using mixed model analysis?; 4. Logistic mixed model analysis; 5. Mixed model analysis with other outcomes; 6. Explaining differences between groups; 7. Multivariable modelling; 8. Predictions based on mixed model analysis; 9. Mixed model analysis for longitudinal data; 10. Multivariate mixed model analysis; 11. Sample size calculations; 12. Some loose ends.
£47.49
Cambridge University Press Statistics for the Social Sciences
Book SynopsisThe second edition of Statistics for the Social Sciences prepares students from a wide range of disciplines to interpret and learn the statistical methods critical to their field of study. By using the General Linear Model (GLM), the author builds a foundation that enables students to see how statistical methods are interrelated enabling them to build on the basic skills. The author makes statistics relevant to students'' varying majors by using fascinating real-life examples from the social sciences. Students who use this edition will benefit from clear explanations, warnings against common erroneous beliefs about statistics, and the latest developments in the philosophy, reporting, and practice of statistics in the social sciences. The textbook is packed with helpful pedagogical features including learning goals, guided practice, and reflection questions.Trade Review'Dr Warne's gift for teaching statistics is apparent in his writing of this book. Indeed, I wish I had this book when I was a student. His use of the General Linear Model as a schema for understanding how statistical methods are interrelated sets the book apart from others.' Leena J. Landmark, Associate Professor of Special Education, Sam Houston State University, USATable of Contents1. Statistics and Models; 2. Levels of Data; 3. Models of Central Tendency and Variability; 4. Visual Models; 5. Linear Transformations and z-Scores; 6. Probability and the Central Limit Theorem; 7. Null Hypothesis Statistical Significance Testing and z-Tests; 8. One-Sample t-Tests; 9. Paired-Samples t-Tests; 10. Unpaired Two-Sample t-Tests; 11. Analysis of Variance; 12. Correlation; 13. Regression; 14. Chi-Squared Test; 15 Applying Statistics to Research, and Advanced Statistical Methods.
£59.84
Cambridge University Press Dicing with Death
Book SynopsisAs a result of the COVID-19 pandemic, medical statistics and public health data have become staples of newsfeeds worldwide, with infection rates, deaths, case fatality and the mysterious R figure featuring regularly. However, we don''t all have the statistical background needed to translate this information into knowledge. In this lively account, Stephen Senn explains these statistical phenomena and demonstrates how statistics is essential to making rational decisions about medical care. The second edition has been thoroughly updated to cover developments of the last two decades and includes a new chapter on medical statistical challenges of COVID-19, along with additional material on infectious disease modelling and representation of women in clinical trials. Senn entertains with anecdotes, puzzles and paradoxes, while tackling big themes including: clinical trials and the development of medicines, life tables, vaccines and their risks or lack of them, smoking and lung cancer, and even the power of prayer.Trade Review'The COVID pandemic has shown the power of statistics to save millions of lives by revealing 'what works'. Yet statistical methods have a deeply controversial history, and provoke sometimes bitter debate to this day. Professor Stephen Senn is renowned for his brilliant insights on the subject, and in Dicing with Death he offers us a series of fascinating journeys through its vast and varied landscape.' Robert Matthews, Visiting Professor Aston University and author of Chancing It: The Laws of Chance and How They Can Work for YouTable of Contents1. Circling the square; 2. The diceman cometh; 3. Trials of life; 4. Of dice and men; 5. Sex and the single patient; 6. A hale view of pills (and other matters); 7. Time's tables; 8. A dip in the pool; 9. The things that bug us; 10. The law is a ass; 11. The empire of the sum; 12. Going viral; Notes; Index.
£19.99
John Wiley & Sons Inc Statistical Data Analytics
Book SynopsisA comprehensive introduction to statistical methods for data mining and knowledge discovery.Table of ContentsPreface xiii Part I Background: Introductory Statistical Analytics 1 1 Data analytics and data mining 3 1.1 Knowledge discovery: finding structure in data 3 1.2 Data quality versus data quantity 5 1.3 Statistical modeling versus statistical description 7 2 Basic probability and statistical distributions 10 2.1 Concepts in probability 10 2.1.1 Probability rules 11 2.1.2 Random variables and probability functions 12 2.1.3 Means, variances, and expected values 17 2.1.4 Median, quartiles, and quantiles 18 2.1.5 Bivariate expected values, covariance, and correlation 20 2.2 Multiple random variables∗ 21 2.3 Univariate families of distributions 23 2.3.1 Binomial distribution 23 2.3.2 Poisson distribution 26 2.3.3 Geometric distribution 27 2.3.4 Negative binomial distribution 27 2.3.5 Discrete uniform distribution 28 2.3.6 Continuous uniform distribution 29 2.3.7 Exponential distribution 29 2.3.8 Gamma and chi-square distributions 30 2.3.9 Normal (Gaussian) distribution 32 2.3.10 Distributions derived from normal 37 2.3.11 The exponential family 41 3 Data manipulation 49 3.1 Random sampling 49 3.2 Data types 51 3.3 Data summarization 52 3.3.1 Means, medians, and central tendency 52 3.3.2 Summarizing variation 56 3.3.3 Summarizing (bivariate) correlation 59 3.4 Data diagnostics and data transformation 60 3.4.1 Outlier analysis 60 3.4.2 Entropy∗ 62 3.4.3 Data transformation 64 3.5 Simple smoothing techniques 65 3.5.1 Binning 66 3.5.2 Moving averages∗ 67 3.5.3 Exponential smoothing∗ 69 4 Data visualization and statistical graphics 76 4.1 Univariate visualization 77 4.1.1 Strip charts and dot plots 77 4.1.2 Boxplots 79 4.1.3 Stem-and-leaf plots 81 4.1.4 Histograms and density estimators 83 4.1.5 Quantile plots 87 4.2 Bivariate and multivariate visualization 89 4.2.1 Pie charts and bar charts 90 4.2.2 Multiple boxplots and QQ plots 95 4.2.3 Scatterplots and bubble plots 98 4.2.4 Heatmaps 102 4.2.5 Time series plots∗ 105 5 Statistical inference 115 5.1 Parameters and likelihood 115 5.2 Point estimation 117 5.2.1 Bias 118 5.2.2 The method of moments 118 5.2.3 Least squares/weighted least squares 119 5.2.4 Maximum likelihood∗ 120 5.3 Interval estimation 123 5.3.1 Confidence intervals 123 5.3.2 Single-sample intervals for normal (Gaussian) parameters 124 5.3.3 Two-sample intervals for normal (Gaussian) parameters 128 5.3.4 Wald intervals and likelihood intervals∗ 131 5.3.5 Delta method intervals∗ 135 5.3.6 Bootstrap intervals∗ 137 5.4 Testing hypotheses 138 5.4.1 Single-sample tests for normal (Gaussian) parameters 140 5.4.2 Two-sample tests for normal (Gaussian) parameters 142 5.4.3 Walds tests, likelihood ratio tests, and ‘exact’ tests∗ 145 5.5 Multiple inferences∗ 148 5.5.1 Bonferroni multiplicity adjustment 149 5.5.2 False discovery rate 151 Part II Statistical Learning and Data Analytics 161 6 Techniques for supervised learning: simple linear regression 163 6.1 What is “supervised learning?” 163 6.2 Simple linear regression 164 6.2.1 The simple linear model 164 6.2.2 Multiple inferences and simultaneous confidence bands 171 6.3 Regression diagnostics 175 6.4 Weighted least squares (WLS) regression 184 6.5 Correlation analysis 187 6.5.1 The correlation coefficient 187 6.5.2 Rank correlation 190 7 Techniques for supervised learning: multiple linear regression 198 7.1 Multiple linear regression 198 7.1.1 Matrix formulation 199 7.1.2 Weighted least squares for the MLR model 200 7.1.3 Inferences under the MLR model 201 7.1.4 Multicollinearity 208 7.2 Polynomial regression 210 7.3 Feature selection 211 7.3.1 R2p plots 212 7.3.2 Information criteria: AIC and BIC 215 7.3.3 Automated variable selection 216 7.4 Alternative regression methods∗ 223 7.4.1 Loess 224 7.4.2 Regularization: ridge regression 230 7.4.3 Regularization and variable selection: the Lasso 238 7.5 Qualitative predictors: ANOVA models 242 8 Supervised learning: generalized linear models 258 8.1 Extending the linear regression model 258 8.1.1 Nonnormal data and the exponential family 258 8.1.2 Link functions 259 8.2 Technical details for GLiMs∗ 259 8.2.1 Estimation 260 8.2.2 The deviance function 261 8.2.3 Residuals 262 8.2.4 Inference and model assessment 264 8.3 Selected forms of GLiMs 265 8.3.1 Logistic regression and binary-data GLiMs 265 8.3.2 Trend testing with proportion data 271 8.3.3 Contingency tables and log-linear models 273 8.3.4 Gamma regression models 281 9 Supervised learning: classification 291 9.1 Binary classification via logistic regression 292 9.1.1 Logistic discriminants 292 9.1.2 Discriminant rule accuracy 296 9.1.3 ROC curves 297 9.2 Linear discriminant analysis (LDA) 297 9.2.1 Linear discriminant functions 297 9.2.2 Bayes discriminant/classification rules 302 9.2.3 Bayesian classification with normal data 303 9.2.4 Naïve Bayes classifiers 308 9.3 k-Nearest neighbor classifiers 308 9.4 Tree-based methods 312 9.4.1 Classification trees 312 9.4.2 Pruning 314 9.4.3 Boosting 321 9.4.4 Regression trees 321 9.5 Support vector machines∗ 322 9.5.1 Separable data 322 9.5.2 Nonseparable data 325 9.5.3 Kernel transformations 326 10 Techniques for unsupervised learning: dimension reduction 341 10.1 Unsupervised versus supervised learning 341 10.2 Principal component analysis 342 10.2.1 Principal components 342 10.2.2 Implementing a PCA 344 10.3 Exploratory factor analysis 351 10.3.1 The factor analytic model 351 10.3.2 Principal factor estimation 353 10.3.3 Maximum likelihood estimation 354 10.3.4 Selecting the number of factors 355 10.3.5 Factor rotation 356 10.3.6 Implementing an EFA 357 10.4 Canonical correlation analysis∗ 361 11 Techniques for unsupervised learning: clustering and association 373 11.1 Cluster analysis 373 11.1.1 Hierarchical clustering 376 11.1.2 Partitioned clustering 384 11.2 Association rules/market basket analysis 395 11.2.1 Association rules for binary observations 396 11.2.2 Measures of rule quality 397 11.2.3 The Apriori algorithm 398 11.2.4 Statistical measures of association quality 402 A Matrix manipulation 411 A.1 Vectors and matrices 411 A.2 Matrix algebra 412 A.3 Matrix inversion 414 A.4 Quadratic forms 415 A.5 Eigenvalues and eigenvectors 415 A.6 Matrix factorizations 416 A.6.1 QR decomposition 417 A.6.2 Spectral decomposition 417 A.6.3 Matrix square root 417 A.6.4 Singular value decomposition 418 A.7 Statistics via matrix operations 419 B Brief introduction to R 421 B.1 Data entry and manipulation 422 B.2 A turbo-charged calculator 426 B.3 R functions 427 B.3.1 Inbuilt R functions 427 B.3.2 Flow control 429 B.3.3 User-defined functions 429 B.4 R packages 430 References 432 Index 453
£73.76
John Wiley & Sons Inc Statistics
Book Synopsis...I know of no better book of its kind... (Journal of the Royal Statistical Society, Vol 169 (1), January 2006) A revised and updated edition of this bestselling introductory textbook to statistical analysis using the leading free software package R This new edition of a bestselling title offers a concise introduction to a broad array of statistical methods, at a level that is elementary enough to appeal to awide range of disciplines. Step-by-step instructionshelp the non-statistician to fully understand the methodology. The book covers the full range of statistical techniques likely to be needed to analyse the data from research projects, including elementary material like t--tests and chi--squared tests, intermediate methods like regression and analysis of variance, and more advanced techniques like generalized linear modelling. Includes numerous worked examples and exercises within each chapter.Table of ContentsPreface xi Chapter 1 Fundamentals 1 Everything Varies 2 Significance 3 Good and Bad Hypotheses 3 Null Hypotheses 3 p Values 3 Interpretation 4 Model Choice 4 Statistical Modelling 5 Maximum Likelihood 6 Experimental Design 7 The Principle of Parsimony (Occam’s Razor) 8 Observation, Theory and Experiment 8 Controls 8 Replication: It’s the ns that Justify the Means 8 How Many Replicates? 9 Power 9 Randomization 10 Strong Inference 14 Weak Inference 14 How Long to Go On? 14 Pseudoreplication 15 Initial Conditions 16 Orthogonal Designs and Non-Orthogonal Observational Data 16 Aliasing 16 Multiple Comparisons 17 Summary of Statistical Models in R 18 Organizing Your Work 19 Housekeeping within R 20 References 22 Further Reading 22 Chapter 2 Dataframes 23 Selecting Parts of a Dataframe: Subscripts 26 Sorting 27 Summarizing the Content of Dataframes 29 Summarizing by Explanatory Variables 30 First Things First: Get to Know Your Data 31 Relationships 34 Looking for Interactions between Continuous Variables 36 Graphics to Help with Multiple Regression 39 Interactions Involving Categorical Variables 39 Further Reading 41 Chapter 3 Central Tendency 42 Further Reading 49 Chapter 4 Variance 50 Degrees of Freedom 53 Variance 53 Variance: A Worked Example 55 Variance and Sample Size 58 Using Variance 59 A Measure of Unreliability 60 Confidence Intervals 61 Bootstrap 62 Non-constant Variance: Heteroscedasticity 65 Further Reading 65 Chapter 5 Single Samples 66 Data Summary in the One-Sample Case 66 The Normal Distribution 70 Calculations Using z of the Normal Distribution 76 Plots for Testing Normality of Single Samples 79 Inference in the One-Sample Case 81 Bootstrap in Hypothesis Testing with Single Samples 81 Student’s t Distribution 82 Higher-Order Moments of a Distribution 83 Skew 84 Kurtosis 86 Reference 87 Further Reading 87 Chapter 6 Two Samples 88 Comparing Two Variances 88 Comparing Two Means 90 Student’s t Test 91 Wilcoxon Rank-Sum Test 95 Tests on Paired Samples 97 The Binomial Test 98 Binomial Tests to Compare Two Proportions 100 Chi-Squared Contingency Tables 100 Fisher’s Exact Test 105 Correlation and Covariance 108 Correlation and the Variance of Differences between Variables 110 Scale-Dependent Correlations 112 Reference 113 Further Reading 113 Chapter 7 Regression 114 Linear Regression 116 Linear Regression in R 117 Calculations Involved in Linear Regression 122 Partitioning Sums of Squares in Regression: SSY = SSR + SSE 125 Measuring the Degree of Fit, r 2 133 Model Checking 134 Transformation 135 Polynomial Regression 140 Non-Linear Regression 142 Generalized Additive Models 146 Influence 148 Further Reading 149 Chapter 8 Analysis of Variance 150 One-Way ANOVA 150 Shortcut Formulas 157 Effect Sizes 159 Plots for Interpreting One-Way ANOVA 162 Factorial Experiments 168 Pseudoreplication: Nested Designs and Split Plots 173 Split-Plot Experiments 174 Random Effects and Nested Designs 176 Fixed or Random Effects? 177 Removing the Pseudoreplication 178 Analysis of Longitudinal Data 178 Derived Variable Analysis 179 Dealing with Pseudoreplication 179 Variance Components Analysis (VCA) 183 References 184 Further Reading 184 Chapter 9 Analysis of Covariance 185 Further Reading 192 Chapter 10 Multiple Regression 193 The Steps Involved in Model Simplification 195 Caveats 196 Order of Deletion 196 Carrying Out a Multiple Regression 197 A Trickier Example 203 Further Reading 211 Chapter 11 Contrasts 212 Contrast Coefficients 213 An Example of Contrasts in R 214 A Priori Contrasts 215 Treatment Contrasts 216 Model Simplification by Stepwise Deletion 218 Contrast Sums of Squares by Hand 222 The Three Kinds of Contrasts Compared 224 Reference 225 Further Reading 225 Chapter 12 Other Response Variables 226 Introduction to Generalized Linear Models 228 The Error Structure 229 The Linear Predictor 229 Fitted Values 230 A General Measure of Variability 230 The Link Function 231 Canonical Link Functions 232 Akaike’s Information Criterion (AIC) as a Measure of the Fit of a Model 233 Further Reading 233 Chapter 13 Count Data 234 A Regression with Poisson Errors 234 Analysis of Deviance with Count Data 237 The Danger of Contingency Tables 244 Analysis of Covariance with Count Data 247 Frequency Distributions 250 Further Reading 255 Chapter 14 Proportion Data 256 Analyses of Data on One and Two Proportions 257 Averages of Proportions 257 Count Data on Proportions 257 Odds 259 Overdispersion and Hypothesis Testing 260 Applications 261 Logistic Regression with Binomial Errors 261 Proportion Data with Categorical Explanatory Variables 264 Analysis of Covariance with Binomial Data 269 Further Reading 272 Chapter 15 Binary Response Variable 273 Incidence Functions 275 ANCOVA with a Binary Response Variable 279 Further Reading 284 Chapter 16 Death and Failure Data 285 Survival Analysis with Censoring 287 Further Reading 290 Appendix Essentials of the R Language 291 R as a Calculator 291 Built-in Functions 292 Numbers with Exponents 294 Modulo and Integer Quotients 294 Assignment 295 Rounding 295 Infinity and Things that Are Not a Number (NaN) 296 Missing Values (NA) 297 Operators 298 Creating a Vector 298 Named Elements within Vectors 299 Vector Functions 299 Summary Information from Vectors by Groups 300 Subscripts and Indices 301 Working with Vectors and Logical Subscripts 301 Addresses within Vectors 304 Trimming Vectors Using Negative Subscripts 304 Logical Arithmetic 305 Repeats 305 Generate Factor Levels 306 Generating Regular Sequences of Numbers 306 Matrices 307 Character Strings 309 Writing Functions in R 310 Arithmetic Mean of a Single Sample 310 Median of a Single Sample 310 Loops and Repeats 311 The ifelse Function 312 Evaluating Functions with apply 312 Testing for Equality 313 Testing and Coercing in R 314 Dates and Times in R 315 Calculations with Dates and Times 319 Understanding the Structure of an R Object Using str 320 Reference 322 Further Reading 322 Index 323
£31.30
Taylor & Francis Ltd Financial Mathematics
Book SynopsisThe book has been tested and refined through years of classroom teaching experience. With an abundance of examples, problems, and fully worked out solutions, the text introduces the financial theory and relevant mathematical methods in a mathematically rigorous yet engaging way. This textbook provides complete coverage of continuous-time financial models that form the cornerstones of financial derivative pricing theory. Unlike similar texts in the field, this one presents multiple problem-solving approaches, linking related comprehensive techniques for pricing different types of financial derivatives.Key features: In-depth coverage of continuous-time theory and methodology Numerous, fully worked out examples and exercises in every chapter Mathematically rigorous and consistent, yet bridging various basic and more advanced concepts Judicious balance of financial theory and mathematiTable of ContentsPart I: Stochastic Calculus with Brownian Motion. 1. One-Dimensional Brownian Motion and Related Processes. 2. Introduction to Continuous-Time Stochastic Calculus. Part II Continuous-Time Modelling. 3. Risk-Neutral Pricing in the (B; S) Economy: One Underlying Stock. 4. Risk-Neutral Pricing in a Multi-Asset Economy. 5. American Options. 6. Interest-Rate Modelling and Derivative Pricing. 7. Alternative Models of Asset Price Dynamics. A. Essentials of General Probability Theory. B. Some Useful Integral (Expectation) Identities and Symmetry Properties of Normal Random Variables. C. Answers and Hints to Exercises. D. Glossary of Symbols and Abbreviations. Greek Alphabet. References. Index.
£82.64
