Probability and statistics Books
Taylor & Francis Ltd Randomization Bootstrap and Monte Carlo Methods
Book SynopsisModern computer-intensive statistical methods play a key role in solving many problems across a wide range of scientific disciplines. Like its bestselling predecessors, the fourth edition of Randomization, Bootstrap and Monte Carlo Methods in Biology illustrates a large number of statistical methods with an emphasis on biological applications. The focus is now on the use of randomization, bootstrapping, and Monte Carlo methods in constructing confidence intervals and doing tests of significance. The text provides comprehensive coverage of computer-intensive applications, with data sets available online.Features Presents an overview of computer-intensive statistical methods and applications in biology Covers a wide range of methods including bootstrap, Monte Carlo, ANOVA, regression, and Bayesian methods Makes it easy for biologists, researchers, and students to understand the methods used Trade Review"...This book deals with statistical data simulations in biology...It should be noted that the presentation of the book contains a lot of explanations and justifications that are not limited by mathematical formula. Thus, a biologist can easily understand the basic idea and approach of any statistical method discussed in the book...The book...is very well structured; the presentation of the material is clear and consistent. There are many illustrative examples and exercises. I enjoyed reading this book, and it is clearly included in the list of books that I highly recommend for study in the training of specialists in the field of biostatistics."- Taras Lukashiv, ISCB News, June 2021 Table of Contents1.Randomization2.The Bootstrap3.Monte Carlo Methods4.Some General Considerations5.One- and Two-Sample Tests6.Analysis of Variance7.Regression Analysis8.Distance Matrices and Spatial Data9.Other Analyses on Spatial Data10.Time Series11.Survival and Growth Data12.Non-Standard Situations13.Bayesian Methods14.Conclusion and Final Comments15.Appendix: Software for Computer-Intensive Statistics
£45.99
Taylor & Francis Ltd Computational Aspects of Psychometric Methods
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£137.75
Taylor & Francis Ltd Computational Aspects of Psychometric Methods
Book SynopsisThis book covers computational aspects of psychometric methods involved in developing measurement instruments and analyzing measurement data in social sciences. It covers main topics of psychometrics such as validity, reliability, item analysis, and item response theory models. Trade Review"This book is an excellent combination of introductory and recent advanced psychometric developments with implemented and illustrated examples using R. The book is clearly written and covers man different topics but is especially interesting as R code are given so the reader can learn how to perform the described analyses. Several datasets are given, and the examples are well explained to help readers to learn the methods. The methods in the book include, but are not limited to classical test theory, item response theory (IRT), item analysis, and differential item functioning. I highly recommend this book, both as course book when teaching psychometrics but also for researchers who wants to perform advanced analyses."- Marie Wiberg, Professor, Umeå University"This book is both a comprehensive introduction to psychometrics and a practical guide to implement the methods using R. It covers a wide range of topics, always providing real-case applications and the R code needed to analyse the data. Remarkable strengths of the book are the large variety of datasets used as examples and the fine balance between theory and applications. The book is suitable as a textbook for Psychometrics courses, and it can be of interest to researchers as well."- Michela Battauz, Associate Professor, University of Udine."I have been instructionally using the R package ShinyItemAnalysis, written by the lead author and her team, for several years and am pleased to see this book appear. It is written in a manner that is clear and inviting to the reader and the mathematics necessary to understand the text is mostly within the reach of someone who has taken introductory statistics, regression, and categorical data. Similarly, the necessary R is also kept fairly basic. In addition to item response theory (IRT), the text contains content on topics such as regression-based item analysis, item bias, classical test theory, and computerized adaptive testing. As such, it would be an excellent addition to an IRT course using R. An instructor using a different text but needing examples and exercises in R would also benefit, as would a motivated scholar undertaking self-study. In sum, I highly recommend this book."- Jay Verkuilen, Associate Professor, The City University of New York.“This book stands as an exceptional resource for researchers and students with a keen interest in the fields of Psychometrics and measurement, with a thorough, accessible and interactive approach to allow the reader to understand these complex concepts and the tools associated with them. The book tackles a challenging list of topics and provides guidance and explanation on how to grasp all the steps of the analysis using graphical and computational tools. The teaching of the methods is accomplished with the use of well prepared and documented R code and information on packages as well as the ShinyItemAnalysis application. The book is suitable to researchers and students at all levels including researchers new to the fields of measurement and computational psychometrics but also advanced users who are interested in implementing and learning more advanced tools. A well written book with an extensive material for learning and practice.”- Irini Moustaki, Professor in Social Statistics, London School of Economics."Impressively, Martinková and Hladká appeals to a broad audience of researchers, ranging from beginners to experts from a variety of disciplines. They introduce key concepts in psychometrics through both written descriptions and sample code, with interactive companion material in the application ShinyItemAnalysis and on github. Examples draw from a variety of data sets that cover entrance exams, learning assessments, the process of peer review, and peoples’ personalities, among others. I appreciate their helpful approach of starting with the basics—here, analyses such as t-tests, ANOVAs, and classical testing theory—and then move on to more complex ideas like Item Response Theory and Differential Item Functioning analysis. Martinková and Hladká explain the basics for historical reasons, as well as because they can be sufficient; when they cover more complex analyses, they also advise researchers about when those techniques might be necessary. Within each chapter, the authors discuss the underlying analytical problems and concepts, then dive into the mathematics of those concepts, and conclude with practical application of the concepts. In addition to the examples mentioned in the text, the authors include a final section with exercises for each chapter. I recommend Martinková and Hladká for anyone teaching or learning psychometrics."- Rebecca M. Price, Professor, School of Interdisciplinary Arts and Sciences, University of Washington Bothell Table of Contents1. Introduction 2. Validity 3. Internal structure of test and factor analysis 4. Reliability 5. Traditional item analysis 6. Item analysis with regression models 7. Item response theory models 8. More complex IRT models 9. Differential item functioning 10. Outlook on applications and more advanced psychometric topics Appendix A. Introduction to R Appendix B. Descriptive statistics Appendix C. Distributions of random variables Appendix D. Measurement data in ShinyItemAnalysis Appendix E. Exercises
£54.14
Taylor & Francis Ltd Data Science
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£49.99
Taylor & Francis Ltd Data Science
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£118.75
Taylor & Francis Ltd TreeBased Methods for Statistical Learning in R
Book SynopsisTree-based Methods for Statistical Learning in R provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus, both conventional and contemporary. Building a strong foundation for how individual decision trees work will help readers better understand tree-based ensembles at a deeper level, which lie at the cutting edge of modern statistical and machine learning methodology.The book follows up most ideas and mathematical concepts with code-based examples in the R statistical language; with an emphasis on using as few external packages as possible. For example, users will be exposed to writing their own random forest and gradient tree boosting functions using simple for loops and basic tree fitting software (like rpart and party/partykit), and more. The core chapters also end with a detailed section on relevant softwTrade ReviewTree-based algorithms have been a workhorse for data science teams for decades, but the data science field has lacked an all-encompassing review of trees - and their modern variants like XGBoost - until now. Greenwell has written the ultimate guide for tree-based methods: how they work, their pitfalls, and alternative solutions. He puts it all together in a readable and immediately usable book. You're guaranteed to learn new tips and tricks to help your data science team. -Alex Gutman, Director of Data Science, Author: Becoming a Data Head"Here’s a new title that is a “must have” for any data scientist who uses the R language. It’s a wonderful learning resource for tree-based techniques in statistical learning, one that’s become my go-to text when I find the need to do a deep dive into various ML topic areas for my work."Daniel D. Gutierrez, Editor-in-Chief for insideBIGDATA, USA, insideBIGDATA, February 2023Table of Contents1 Introduction 2 Binary recursive partitioning with CART 3 Conditional inference trees 4 "The hitchhiker’s GUIDE to modern decision trees" 5 Ensemble algorithms 6 Peeking inside the “black box”: post-hoc interpretability 7 Random forests 8 Gradient boosting machines
£80.74
Taylor & Francis Ltd SAS Coding Primer and Reference Guide
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£133.00
Taylor & Francis Ltd Supervised Machine Learning
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£45.99
Taylor & Francis Ltd Probability Choice and Reason
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£52.24
Taylor & Francis Ltd Probability Choice and Reason
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£137.75
Taylor & Francis Ltd Handbook of Bayesian Variable Selection
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£155.00
Taylor & Francis Ltd Handbook of Bayesian Variable Selection
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£56.04
Taylor & Francis Ltd Spatial Predictive Modeling with R
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£105.00
Taylor & Francis Ltd An Introduction to Acceptance Sampling and SPC with R
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£47.49
Taylor & Francis Ltd Applied Biostatistical Principles and Concepts
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£37.04
Taylor & Francis Ltd Applied Epidemiologic Principles and Concepts
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£37.04
Taylor & Francis Ltd Mathematics and Programming for Machine Learning with R From the Ground Up
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£94.99
Taylor & Francis Ltd Prognostics and Remaining Useful Life RUL
Book SynopsisMaintenance combines various methods, tools, and techniques in a bid to reduce maintenance costs while increasing the reliability, availability, and security of equipment. Condition-based maintenance (CBM) is one such method, and prognostics forms a key element of a CBM program based on mathematical models for predicting remaining useful life (RUL). Prognostics and Remaining Useful Life (RUL) Estimation: Predicting with Confidence compares the techniques and models used to estimate the RUL of different assets, including a review of the relevant literature on prognostic techniques and their use in the industrial field. This book describes different approaches and prognosis methods for different assets backed up by appropriate case studies.FEATURES Presents a compendium of RUL estimation methods and technologies used in predictive maintenance Describes different approaches and prognosis methods for different assets Table of Contents1. Information in Maintenance 2. Predictive Maintenance Programs and Servitization Maintenance as a Service (MaaS) Creating Value through Prognosis Capabilities 3. RUL Estimation Powered by Data-Driven Techniques 4. Context Awareness and Situation Awareness in Prognostics 5. Black Swans and Physics of Failure 6. Hybrid Prognostics Combining Physics-Based and Data-Driven Approaches 7. Prognosis in Prescriptive Analytics 8. Uncertainty Management and the Confidence of RUL Predictions 9. RUL Estimation of Dynamic and Static Assets 10. Principles of Digital Twin 11. Application of Prognosis in Industry, Energy, and Transportation
£166.25
Taylor & Francis Incomparable Values
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£128.25
Taylor & Francis Ltd Simultaneous Global New Drug Development
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£142.50
Taylor & Francis Ltd StateSpace Methods for Time Series Analysis
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£47.49
Taylor & Francis Ltd Weakly Stationary Random Fields Invariant Subspaces and Applications
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£43.69
Taylor & Francis Ltd Stochastic H2H Control
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£42.74
Taylor & Francis Ltd Event Mining
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£42.74
Taylor & Francis Ltd Generalized Sylvester Equations
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£42.74
Taylor & Francis Ltd Statistical Reinforcement Learning
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£42.74
Taylor & Francis Ltd Quantitative Evaluation of Safety in Drug Development
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£43.69
Taylor & Francis Ltd Statistical Inference Based on Divergence Measures
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£43.69
Taylor & Francis Ltd Handbook of Stochastic Analysis and Applications
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£43.69
Taylor & Francis Ltd Multivariate Quality Control
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£43.69
Taylor & Francis Ltd White Noise Distribution Theory
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£43.69
Taylor & Francis Ltd Truncated and Censored Samples
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£43.69
Taylor & Francis Ltd Point Processes and Their Statistical Inference
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£45.99
Taylor & Francis Ltd A Primer in Probability
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£45.99
Taylor & Francis Ltd Robust Regression
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£43.69
Taylor & Francis Ltd Interface between Regulation and Statistics in Drug Development
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£43.69
Taylor & Francis Ltd Handbook of Matching and Weighting Adjustments for Causal Inference
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£195.00
Taylor & Francis Ltd Analyzing Spatial Models of Choice and Judgment
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£45.59
Taylor & Francis Ltd AIBased Metaheuristics for Information Security and Digital Media
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£77.99
Taylor & Francis Ltd Interpreting Statistics for Beginners
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£35.99
Taylor & Francis Ltd Optimal Decision Making in Operations Research
Book SynopsisThe book provides insights in the decision-making for implementing strategies in various spheres of real-world issues. It integrates optimal policies in various decisionmaking problems and serves as a reference for researchers and industrial practitioners. Furthermore, the book provides sound knowledge of modelling of real-world problems and solution procedure using the various optimisation and statistical techniques for making optimal decisions. The book is meant for teachers, students, researchers and industrialists who are working in the field of materials science, especially operations research and applied statistics. Table of Contents1. A New Version of the Generalized Rayleigh Distribution with Copula, Properties, Applications and Different Methods of Estimation 2. Expanding the Burr X Model: Properties, Copula, Real Data Modeling and Different Methods of Estimation 3. Transmuted Burr Type X Model with Applications to Life Time Data 4. Monitoring Patients Blood Level through Enhanced Control Chart 5. Goodness of Fit in Parametric and Non-parametric Econometric Models 6. Stochastic Models for Cancer Progression and its Optimal Programming for Control with Chemotherapy 7. A New Unrelated Question Model with Two Questions Per Card 8. Hybrid of Simple Model and a New Unrelated Question Model for Two Sensitive Characteristics 9. Hybrid of Crossed Model and a New Unrelated Question Model for Two Sensitive Characteristics 10. Modified Regression Type Estimator by Ingeniously Utilizing Probabilities for more Efficient Results in Randomized Response Sampling 11. Ratio and Regression Type Estimators for a New Measure of Coefficient of Dispersion Relative to the Empirical Mode 12. Class of Exponential Ratio Type Estimator for Population Mean in Adaptive Cluster Sampling 13. An Inventory Model for Substitutable Deteriorating Products under Fuzzy and Cloud Fuzzy Demand Rate 14. Co-ordinated Selling Price and Replenishment Policies for Duopoly Retailers under Quadratic Demand and Deteriorating Nature of Items15. Quadratic Programming Approach for the Optimal Multi-objective Transportation Problem 16. Analyzing Multi-Objective Fixed-Charge Solid Transportation Problem under Rough and Fuzzy-Rough Environments 17. Overall Shale Gas Water Management: A Neutrosophic Optimization Approach 18. Memory Effect on an EOQ Model with Price Dependant Demand and Deterioration 19. Optimality Conditions of an Unconstrained Imprecise Optimization Problem via Interval Order Relation 20. Power Comparison of Different Goodness of Fit Tests for Beta Generalized Weibull Distribution 21. On the Transmuted Modified Lindley Distribution: Theory and Applications to Lifetime Data 22. Adjusted Bias and Risk for Estimating Treatment Effect after Selection with an Application in Idiopathic Osteoporosis 23. Validity Judgement of an EOQ Model using Phi-coefficient 24. Uncertain Chance-Constrained Multi-Objective Geometric Programming Problem 25. Optimal Decision Making for the Prediction of Diabetic Retinopathy in Type 2 Diabetes Mellitus Patients
£49.39
Taylor & Francis Ltd Multidimensional Stationary Time Series
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£43.69
Taylor & Francis Ltd Interpreting Statistics for Beginners
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£128.25
Taylor & Francis Ltd Trace Environmental Quantitative Analysis
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£73.14
Taylor & Francis Ltd Applied Linear Regression for Longitudinal Data
Book SynopsisThis book introduces best practices in longitudinal data analysis at intermediate level, with a minimum number of formulas without sacrificing depths. It meets the need to understand statistical concepts of longitudinal data analysis by visualizing important techniques instead of using abstract mathematical formulas. Different solutions such as multiple imputation are explained conceptually and consequences of missing observations are clarified using visualization techniques. Key features include the following: Provides datasets and examples online Gives state-of-the-art methods of dealing with missing observations in a non-technical way with a special focus on sensitivity analysis Conceptualises the analysis of comparative (experimental and observational) studies It is the ideal companion for researchers and students in epidemiological, health, and social and behavioral sciences working with longitudinal studies without a mathematical backgrouTrade Review"Overall, the book is well written. It is clear and allows the reader understanding the main concepts behind models for longitudinal data analysis, with few effort from a technical viewpoint. The examples used to illustrate the methods covered in the textbook are numerous and also rather easy to follow. This helps the reader learn how to proceed with a full longitudinal data analysis."Maria Francesca Marino, University of Florence, Italy, The American Statistician, February 2024.Table of Contents1. Scientific Framework of Data Analysis 2. Revisiting and Shortcomings of Standard Linear Regression Models 3. An Introduction to the Analysis of Longitudinal Data 4. Model Building for Longitudinal Data Analysis 5. Analysis of a Pre/Post Measurement Design 6. Analysis of Longitudinal Life-Event Studies 7. Analysis of Longitudinal Experimental Studies
£87.39
Taylor & Francis Ltd Machine Learning
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£52.24
Taylor & Francis Ltd Engineering Design and Mathematical Modelling
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£43.69
Taylor & Francis Ltd Statistics and Machine Learning Methods for EHR Data
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£45.99