Econometrics and economic statistics Books
Taylor & Francis Ltd Environmental Risk Modelling in Banking
Book SynopsisEnvironmental risk directly affects the financial stability of banks since they bear the financial consequences of the loss of liquidity of the entities to which they lend and of the financial penalties imposed resulting from the failure to comply with regulations and for actions taken that are harmful to the natural environment. This book explores the impact of environmental risk on the banking sector and analyzes strategies to mitigate this risk with a special emphasis on the role of modelling. It argues that environmental risk modelling allows banks to estimate the patterns and consequences of environmental risk on their operations, and to take measures within the context of asset and liability management to minimize the likelihood of losses. An important role here is played by the environmental risk modelling methodology as well as the software and mathematical and econometric models used. It examines banks' responses to macroprudential risk, particularly from the point oTable of Contents1. Introduction 2. Environmental risk as a challenge for banking sector 3. Environmental regulations as a framework for environmental risk management in banks 4. Quantitative methods and IT in environmental risk modeling 5. Environmental risk modeling and decision making process 6. The spread of environmental risk and the way to analyze the issue 7. Incorporating environmental risk in banks’ business models 8. Environmental risk and its impact on bankruptcy and insolvency risk
£35.99
Taylor & Francis Ltd Tidy Finance with R
Book SynopsisThis textbook shows how to bring theoretical concepts from finance and econometrics to the data. Focusing on coding and data analysis with R, we show how to conduct research in empirical finance from scratch. We start by introducing the concepts of tidy data and coding principles using the tidyverse family of R packages. Code is provided to prepare common open-source and proprietary financial data sources (CRSP, Compustat, Mergent FISD, TRACE) and organize them in a database. We reuse these data in all the subsequent chapters, which we keep as self-contained as possible. The empirical applications range from key concepts of empirical asset pricing (beta estimation, portfolio sorts, performance analysis, Fama-French factors) to modeling and machine learning applications (fixed effects estimation, clustering standard errors, difference-in-difference estimators, ridge regression, Lasso, Elastic net, random forests, neural networks) and portfolio optimization techniques.HigTable of Contents1. Introduction to Tidy Finance 2. Accessing & Managing Financial Data 3. WRDS, CRSP, and Compustat 4. TRACE and FISD 5. Other Data Providers 6. Beta Estimation 7. Univariate Portfolio Sorts 8. Size Sorts and P-Hacking 9. Value and Bivariate Sorts 10. Replicating Fama and French Factors 11. Fama-MacBeth Regressions 12. Fixed Effects and Clustered Standard Errors 13. Difference in Differences 14. Factor Selection via Machine Learning 15. Option Pricing via Machine Learning 16. Parametric Portfolio Policies 17. Constrained Optimization and Backtesting Appendix A. Cover Design Appendix B. Clean Enhanced TRACE with R
£58.89
Taylor & Francis Ltd AI for Finance
Book SynopsisFinance students and practitioners may ask: can machines learn everything? Could AI help me? Computing students or practitioners may ask: which of my skills could contribute to finance? Where in finance should I pay attention? This book aims to answer these questions. No prior knowledge is expected in AI or finance.Including original research, the book explains the impact of ignoring computation in classical economics; examines the relationship between computing and finance and points out potential misunderstandings between economists and computer scientists; and introduces Directional Change and explains how this can be used.To finance students and practitioners, this book will explain the promise of AI, as well as its limitations. It will cover knowledge representation, modelling, simulation and machine learning, explaining the principles of how they work. To computing students and practitioners, this book will introduce the financial applications in which AI has madTrade Review“This important book is an unusually topical attempt to introduce readers to the relationship between the technical analysis of financial market prices and the automated implementation of its findings. The book will be of considerable interest to those who wish to know about this relationship in an eminently readable form: both professional financial market analysts and those considering future employment in the field.” --Michael Dempster, Professor Emeritus in the Statistical Laboratory at the University of Cambridge“AI is an important part of finance today. Students who want to join the finance industry should read this book. The trained eyes will also find a lot of insights in the book. I cannot think of any other book that teaches computational finance at a beginner's level but at the same time is useful to practitioners.” --Amadeo Alentorn, PhD, Head of Systematic Equities at Jupiter Asset Management"AI for Finance is an excellent primer for experts and newcomers seeking to unlock the potential of AI. The book combines deep thinking with a bird’s eye view of the whole field - the ideal text to get inspired and apply AI. A big thank you to Edward Tsang, a pioneer of AI and quantitative finance, for making the concepts and usage of AI easily accessible to academics and practitioners." --Richard Olsen, Founder and CEO of Lykke, co-founder of OANDA, and pioneer in high frequency finance and fintech“Without a doubt, AI symbolizes the future of finance and, in this important book, Professor Tsang provides an excellent account of its mechanics, concepts and strategies. Books featuring AI in finance are rare so practitioners and students would do well to read it to gain focus and valuable insights into this fast-evolving technology. Congratulations to Professor Tsang for providing a readable and engaging work in a complex technology that will appeal to all levels of readers!” --Dr David Norman, Founder of the TTC Institute"The use of AI/ML in the financial industry is now more than a hype. In financial institutions there are numerous active transformation programs to introduce AI/ML enabled products in areas such as risk, trading and advanced analytics. In this book, Edward, one of the early adopters of AI in finance, has provided an insightful guide for both finance practitioners and academics. I can see this book becoming a major reference in real-world applied AI in finance. Directional Change (Chapter 6) should be of particular interest to data scientists in finance, as how one collects data determines what one can reason about." -- Dr Ali Rais Shaghaghi, Lead Data Scientist at NatWest Group.Table of Contents1. AI-Finance Synergy, 2. Machine Learning Knows No Boundaries?, 3.Machine Learning in Finance, 4. Modelling, Simulation and Machine Learning, 5. Portfolio Optimization, 6. Financial Data: Beyond Time Series, 7. Over the Horizon
£114.00
Taylor & Francis Ltd Big Data Analytics
Book SynopsisSuccessfully navigating the data-driven economy presupposes a certain understanding of the technologies and methods to gain insights from Big Data. This book aims to help data science practitioners to successfully manage the transition to Big Data. Building on familiar content from applied econometrics and business analytics, this book introduces the reader to the basic concepts of Big Data Analytics. The focus of the book is on how to productively apply econometric and machine learning techniques with large, complex data sets, as well as on all the steps involved before analysing the data (data storage, data import, data preparation). The book combines conceptual and theoretical material with the practical application of the concepts using R and SQL. The reader will thus acquire the skills to analyse large data sets, both locally and in the cloud. Various code examples and tutorials, focused on empirical economic and business research, illustrate practical techniques to handleTrade Review“This book is a superb practical guide for data scientists and graduate students in business and economics interested in data analytics. The combination of a clear introduction to the concepts and techniques of big data analytics with examples of how to code these tools makes this book both accessible and practical. I highly recommend this book to anyone seeking to prepare themselves for the ever-evolving world of data analytics in business and economics research.”- Oded Netzer, Vice Dean for Research, Columbia Business School"Ulrich Matter’s book on Big Data Analytics is an ideal resource for academics and corporate practitioners who have had some exposure to data analytics and want to enrich their toolbox to handle Big Data. This monograph sets the scene from many points of view: programming techniques, databases, distributed computing, Big Data handling, visualization, machine learning, and GPU deployment. Even though R has been chosen as the programming language, many techniques discussed in the book are not R-dependent and can be easily translated into other languages and computing environments. The writing style makes this handbook useful both as a main reference in the teaching of a course in related topics as well as an aid for those who want to learn the material independently. The author’s approach is 100% hands-on. Not much attention is paid to the technical aspects involving algorithms; all the focus goes to implementation strategies and to the specificities of the interplay between programming, hardware, databases, and visualization problems that arises in Big Data contexts. The book has been thoroughly tested in classes that the author has been teaching for a number of years, which makes it a safe bet for those looking for a textbook on the topic. I highly recommend it!"- Juan-Pablo Ortega, Head, Division of Mathematical Sciences, Nanyang Technological University Table of ContentsPart 1. Setting the Scene: Analyzing Big Data 1. What is Big in "Big Data"? 2. Approaches to Analyzing Big Data 3. The Two Domains of Big Data Analytics Part 2. Platform: Software and Computing Resources 4. Software: Programming with (Big) Data 5. Hardware: Computing Resources 6. Distributed Systems 7. Cloud Computing Part 3. Components of Big Data Analytics 8. Data Collection and Data Storage 9. Big Data Cleaning and Transformation 10. Descriptive Statistics and Aggregation 11. (Big) Data Visualization Part 4. Application: Topics in Big Data Econometrics 12. Bottlenecks in Everyday Data Analytics Tasks 13. Econometrics with GPUs 14. Regression Analysis and Categorization with Spark and R 15. Large-scale Text Analysis with sparklyr Part 5. Appendices Appendix A. GitHub Appendix B. R Basics Appendix C. Install Hadoop
£39.99
Taylor & Francis Ltd Introduction to Stochastic Calculus Applied to
Book SynopsisSince the publication of the first edition of this book, the area of mathematical finance has grown rapidly, with financial analysts using more sophisticated mathematical concepts, such as stochastic integration, to describe the behavior of markets and to derive computing methods. Maintaining the lucid style of its popular predecessor, Introduction to Stochastic Calculus Applied to Finance, Second Edition incorporates some of these new techniques and concepts to provide an accessible, up-to-date initiation to the field. New to the Second EditionComplements on discrete models, including Rogers'' approach to the fundamental theorem of asset pricing and super-replication in incomplete markets Discussions on local volatility, Dupire''s formula, the change of numéraire techniques, forward measures, and the forward Libor model A new chapter on credit risk modeling An extension of the chapter on simulTrade ReviewThe second edition of this book provides a concise and accessible introduction to the probabilistic techniques needed to understand the most widely used financial models. This edition incorporates many new techniques and concepts to be used to describe the behavior of financial markets. … the solutions obtained using SciLab for computer experiments are available at http://cermics.enpc.fr/~bl/scilab/ These experiments were well designed by the authors based on their teaching and research experience and were found to be effective in communicating these concepts and ideas and enhancing the understanding of readers. … a solid introduction to stochastic approaches used in the financial world. The authors cover many key finance topics … . The book can be used as a reference text by researchers and graduate students in financial mathematics. It also is ideal reading material for practicing financial analysts and consultants using mathematical models for finance.—Technometrics, May 2009, Vol. 51, No. 2 Table of ContentsDiscrete-Time Models. Optimal Stopping Problem and American Options. Brownian Motion and Stochastic Differential Equations. The Black-Scholes Model. Option Pricing and Partial Differential Equations. Interest Rate Models. Asset Models with Jumps. Credit Risk Models. Simulation and Algorithms for Financial Models. Appendix. Bibliography. Index.
£43.99
Taylor & Francis Ltd Forecasting and Analytics with the Augmented
Book SynopsisForecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM) focuses on a time series model in Single Source of Error state space form, called ADAM (Augmented Dynamic Adaptive Model). The book demonstrates a holistic view to forecasting and time series analysis using dynamic models, explaining how a variety of instruments can be used to solve real life problems. At the moment, there is no other tool in R or Python that would be able to model both intermittent and regular demand, would support both ETS and ARIMA, work with explanatory variables, be able to deal with multiple seasonalities (e.g. for hourly demand data) and have a support for automatic selection of orders, components and variables and provide tools for diagnostics and further improvement of the estimated model. ADAM can do all of that in one and the same framework. Given the rising interest in forecasting, ADAM, being able to do all those things, is a useful tool for data scientists, business analTable of Contents1. Introduction 2. Forecasts evaluation 3. Time series components and simple forecasting methods 4. Introduction to ETS 5. Pure additive ADAM ETS 6. Pure multiplicative ADAM ETS 7. General ADAM ETS model 8. Introduction to ARIMA 9. ADAM ARIMA 10. Explanatory variables in ADAM 11. Estimation of ADAM 12. Multiple frequencies in ADAM 13. Intermittent State Space Model 14. Model diagnostics 15. Model selection and combinations in ADAM 16. Handling uncertainty in ADAM 17. Scale model for ADAM 18. Forecasting with ADAM 19. Forecasting functions of the smooth package 20. What’s next?
£87.39
Taylor & Francis Ltd Large Databases in Economic History
Book SynopsisBig data' is now readily available to economic historians, thanks to the digitisation of primary sources, collaborative research linking different data sets, and the publication of databases on the internet. Key economic indicators, such as the consumer price index, can be tracked over long periods, and qualitative information, such as land use, can be converted to a quantitative form. In order to fully exploit these innovations it is necessary to use sophisticated statistical techniques to reveal the patterns hidden in datasets, and this book shows how this can be done.A distinguished group of economic historians have teamed up with younger researchers to pilot the application of new techniques to big data'. Topics addressed in this volume include prices and the standard of living, money supply, credit markets, land values and land use, transport, technological innovation, and business networks. The research spans the medieval, early modern and modern periods. Research methoTrade Review'This book makes applied econometric methods accessible to anyone interested in quantitative economic history' — Helen Paul, University of Southampton, UK.Table of Contents1. Introduction: Research methods for large databases Mark Casson and Nigar Hashimzade 2. Long-run Price Dynamics: The measurement of substitutability between commodities Mark Casson, Nigar Hashimzade and Catherine Casson 3. The Quantity Theory of Money in Historical Perspective Nick Mayhew 4. Medieval Foreign Exchange: A time series analysis Adrian Bell, Chris Brooks and Tony K. Moore 5. Local Property Values in Fourteenth and Fifteenth-century England Margaret Yates, Anna Campbell and Mark Casson 6. Visual Analytics for Large-scale Actor Networks, with an Application to Liverpool Business Networks John Haggerty and Sheryllynne Haggerty 7. Railways and Local Population Growth: Northamptonshire and Rutland, 1801-91 Mark Casson, Leigh Shaw-Taylor, A.E.M. Satchell and E.A. Wrigley 8. Women’s Land Ownership in Nineteenth-century England Janet Casson 9. The Diffusion of Steam Technology in England: Ploughing engines, 1860-1930 Jane McCutchan 10. Industrious Burglars: Funding consumption from property crime Jane Humphries, Sara Horrell and Ken Sneath
£47.49
Taylor & Francis Ltd Applied Regression Analysis
Book SynopsisThis book is an introduction to regression analysis, focusing on the practicalities of doing regression analysis on real-life data.Contrary to other textbooks on regression, this book is based on the idea that you do not necessarily need to know much about statistics and mathematics to get a firm grip on regression and perform it to perfection. This non-technical point of departure is complemented by practical examples of real-life data analysis using statistics software such as Stata, R and SPSS. Parts 1 and 2 of the book cover the basics, such as simple linear regression, multiple linear regression, how to interpret the output from statistics programs, significance testing and the key regression assumptions. Part 3 deals with how to practically handle violations of the classical linear regression assumptions, regression modeling for categorical y-variables and instrumental variable (IV) regression. Part 4 puts the various purposes of, or motivations for, regression iTrade Review“This book finds a rare balance between applied statistical analysis and theory, giving students the confidence to apply regression analysis in their projects, while being aware of the potential pitfalls.” — Johan A. Elkink, Associate Professor in Social Science Research Methods, University College Dublin, Ireland“This book provides a short and bright path to understand the meaning and usefulness of regression analysis. If you are a student or policy maker with limited econometrics skills this book equips you with the right and sufficient skills.” — Dr. Maty Konte, United Nations UniversityTable of ContentsPart 1: The Basics 1. What is regression analysis? 2. Linear regression with a single independent variable 3. Linear regression with several independent variables: Multiple regression Part 2: The Foundations 4. Samples and populations, statistical uncertainty and testing of statistical significance 5. The assumptions of regression analysis Part 3: The Extensions 6. Beyond linear regression: Non-additivity, non-linearity and mediation 7. A categorical dependent variable: Logistic (logit) regression and related methods 8. An ordered (ordinal) dependent variable: Logistic (logit) regression 9. The quest for a causal effect: Instrumental variable (IV) regression Part 4: Regression Purposes, Academic Regression Projects and the Way Ahead 10. Regression purposes in various academic settings and how to perform them 11. The way ahead: Related techniques
£52.24
Taylor & Francis Ltd Probability and Bayesian Modeling
Book SynopsisProbability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus background. The first part of the book provides a broad view of probability including foundations, conditional probability, discrete and continuous distributions, and joint distributions. Statistical inference is presented completely from a Bayesian perspective. The text introduces inference and prediction for a single proportion and a single mean from Normal sampling. After fundamentals of Markov Chain Monte Carlo algorithms are introduced, Bayesian inference is described for hierarchical and regression models including logistic regression. The book presents several case studies motivated by some historical Bayesian studies and the authors' research.This text reflects modern Bayesian statistical practice. Simulation is introduced in all the probability chapters and extensively used in the Bayesian material to simulate frTrade Review"The book can be used by upper undergraduate and graduate students as well as researchers and practitioners in statistics and data science from all disciplines…A background of calculus is required for the reader but no experience in programming is needed. The writing style of the book is extremely reader friendly. It provides numerous illustrative examples, valuable resources, a rich collection of materials, and a memorable learning experience."~Technometrics"Over many years, I have wondered about the following: Should a first undergraduate course in statistics be a Bayesian course? After reading this book, I have come to the conclusion that the answer is…yes!... this is very well written textbook that can also be used as self-learning material for practitioners. It presents a clear, accessible, and entertaining account of the interplay of probability, computations, and statistical inference from the Bayesian perspective."~ISCB NewsTable of Contents1. Introduction, examples and review. 2. Why Bayes? 3. One-parameter models. 4. Monte Carlo approximation. 5. Normal models. 6. Gibbs sampler. 7. Metropolis-Hastings algorithms, BUGS. 8. Bayesian hierarchical modeling. 9. Multivariate normal models. 10. Bayesian linear regression. 11. Bayesian model comparison, variable selection and model selection. 12. Applications.
£80.74
Taylor & Francis Ltd HandsOn Machine Learning with R
Book SynopsisHands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algoTrade Review"Hands-On Machine Learning with R is a great resource for understanding and applying models. Each section provides descriptions and instructions using a wide range of R packages." - Max Kuhn, Machine Learning Software Engineer, RStudio"You can't find a better overview of practical machine learning methods implemented with R."- JD Long, co-author of R Cookbook"Simultaneously approachable, accessible, and rigorous, Hands-On Machine Learning with R offers a balance of theory and implementation that can actually bring you from relative novice to competent practitioner." - Mara Averick, RStudio Dev Advocate"Hands-On Machine Learning with R is a great resource for understanding and applying models. Each section provides descriptions and instructions using a wide range of R packages." - Max Kuhn, Machine Learning Software Engineer, RStudio"You can't find a better overview of practical machine learning methods implemented with R."- JD Long, co-author of R Cookbook"Simultaneously approachable, accessible, and rigorous, Hands-On Machine Learning with R offers a balance of theory and implementation that can actually bring you from relative novice to competent practitioner." - Mara Averick, RStudio Dev Advocate"...The book describes in detail the various methods for solving classification and clustering problems. Functions from many R libraries are compared, which enables the reader to understand their respective advantages and disadvantages. The authors have developed a clear structure to the book that includes a brief description of each model, examples of using the model for specific real-life examples, and discussion of the advantages and disadvantages of the model. This structure is one of the book’s main advantages."- Igor Malyk, ISCB News, July 2020Table of ContentsI FUNDAMENTALS 1. Introduction to Machine Learning 1.1 Supervised learning 1.1.1 Regression problems 1.1.2 Classification problems 1.2 Unsupervised learning 1.3 Roadmap 1.4 The data sets 2. Modeling Process 2.1 Prerequisites 2.2 Data splitting 2.2.1 Simple random sampling 2.2.2 Stratified sampling 2.2.3 Class imbalances 2.3 Creating models in R 2.3.1 Many formula interfaces 2.3.2 Many engines 2.4 Resampling methods 2.4.1 k-fold cross validation 2.4.2 Bootstrapping 2.4.3 Alternatives 2.5 Bias variance trade-off 2.5.1 Bias 2.5.2 Variance 2.5.3 Hyperparameter tuning 2.6 Model evaluation 2.6.1 Regression models 2.6.2 Classification models 2.7 Putting the processes together 3. Feature & Target Engineering 3.1 Prerequisites 3.2 Target engineering 3.3 Dealing with missingness 3.3.1 Visualizing missing values 3.3.2 Imputation 3.4 Feature filtering 3.5 Numeric feature engineering 3.5.1 Skewness 3.5.2 Standardization 3.6 Categorical feature engineering 3.6.1 Lumping 3.6.2 One-hot & dummy encoding 3.6.3 Label encoding 3.6.4 Alternatives 3.7 Dimension reduction 3.8 Proper implementation 3.8.1 Sequential steps 3.8.2 Data leakage 3.8.3 Putting the process together II SUPERVISED LEARNING 4. Linear Regression 4.1 Prerequisites 4.2 Simple linear regression 4.2.1 Estimation 4.2.2 Inference 4.3 Multiple linear regression 4.4 Assessing model accuracy 4.5 Model concerns 4.6 Principal component regression 4.7 Partial least squares 4.8 Feature interpretation 4.9 Final thoughts 5. Logistic Regression 5.1 Prerequisites 5.2 Why logistic regression 5.3 Simple logistic regression 5.4 Multiple logistic regression 5.5 Assessing model accuracy 5.6 Model concerns 5.7 Feature interpretation 5.8 Final thoughts 6. Regularized Regression 6.1 Prerequisites 6.2 Why regularize? 6.2.1 Ridge penalty 6.2.2 Lasso penalty 6.2.3 Elastic nets 6.3 Implementation 6.4 Tuning 6.5 Feature interpretation 6.6 Attrition data 6.7 Final thoughts 7. Multivariate Adaptive Regression Splines 7.1 Prerequisites 7.2 The basic idea 7.2.1 Multivariate regression splines 7.3 Fitting a basic MARS model 7.4 Tuning 7.5 Feature interpretation 7.6 Attrition data 7.7 Final thoughts 8. K-Nearest Neighbors 8.1 Prerequisites 8.2 Measuring similarity 8.2.1 Distance measures 8.2.2 Pre-processing 8.3 Choosing k 8.4 MNIST example 8.5 Final thoughts 9 Decision Trees 9.1 Prerequisites 9.2 Structure 9.3 Partitioning 9.4 How deep? 9.4.1 Early stopping 9.4.2 Pruning 9.5 Ames housing example 9.6 Feature interpretation 9.7 Final thoughts 10. Bagging 10.1 Prerequisites 10.2 Why and when bagging works 10.3 Implementation 10.4 Easily parallelize 10.5 Feature interpretation 10.6 Final thoughts 11. Random Forests 11.1 Prerequisites 11.2 Extending bagging 11.3 Out-of-the-box performance 11.4 Hyperparameters 11.4.1 Number of trees 11.4.2 mtry 11.4.3 Tree complexity 11.4.4 Sampling scheme 11.4.5 Split rule 11.5 Tuning strategies 11.6 Feature interpretation 11.7 Final thoughts 12. Gradient Boosting 12.1 Prerequisites 12.2 How boosting works 12.2.1 A sequential ensemble approach 12.2.2 Gradient descent 12.3 Basic GBM 12.3.1 Hyperparameters 12.3.2 Implementation 12.3.3 General tuning strategy 12.4 Stochastic GBMs 12.4.1 Stochastic hyperparameters 12.4.2 Implementation 12.5 XGBoost 12.5.1 XGBoost hyperparameters 12.5.2 Tuning strategy 12.6 Feature interpretation 12.7 Final thoughts 13. Deep Learning 13.1 Prerequisites 13.2 Why deep learning 13.3 Feedforward DNNs 13.4 Network architecture 13.4.1 Layers and nodes 13.4.2 Activation 13.5 Backpropagation 13.6 Model training 13.7 Model tuning 13.7.1 Model capacity 13.7.2 Batch normalization 13.7.3 Regularization 13.7.4 Adjust learning rate 13.8 Grid Search 13.9 Final thoughts 14. Support Vector Machines 14.1 Prerequisites 14.2 Optimal separating hyperplanes 14.2.1 The hard margin classifier 14.2.2 The soft margin classifier 14.3 The support vector machine 14.3.1 More than two classes 14.3.2 Support vector regression 14.4 Job attrition example 14.4.1 Class weights 14.4.2 Class probabilities 14.5 Feature interpretation 14.6 Final thoughts 15. Stacked Models 15.1 Prerequisites 15.2 The Idea 15.2.1 Common ensemble methods 15.2.2 Super learner algorithm 15.2.3 Available packages 15.3 Stacking existing models 15.4 Stacking a grid search 15.5 Automated machine learning 15.6 Final thoughts 16. Interpretable Machine Learning 16.1 Prerequisites 16.2 The idea 16.2.1 Global interpretation 16.2.2 Local interpretation 16.2.3 Model-specific vs. model-agnostic 16.3 Permutation-based feature importance 16.3.1 Concept 16.3.2 Implementation 16.4 Partial dependence 16.4.1 Concept 16.4.2 Implementation 16.4.3 Alternative uses 16.5 Individual conditional expectation 16.5.1 Concept 16.5.2 Implementation 16.6 Feature interactions 16.6.1 Concept 16.6.2 Implementation 16.6.3 Alternatives 16.7 Local interpretable model-agnostic explanations 16.7.1 Concept 16.7.2 Implementation 16.7.3 Tuning 16.7.4 Alternative uses 16.8 Shapley values 16.8.1 Concept 16.8.2 Implementation 16.8.3 XGBoost and built-in Shapley values 16.9 Localized step-wise procedure 16.9.1 Concept 16.9.2 Implementation 16.10Final thoughts III DIMENSION REDUCTION 17. Principal Components Analysis 17.1 Prerequisites 17.2 The idea 17.3 Finding principal components 17.4 Performing PCA in R 17.5 Selecting the number of principal components 17.5.1 Eigenvalue criterion 17.5.2 Proportion of variance explained criterion 17.5.3 Scree plot criterion 17.6 Final thoughts 18. Generalized Low Rank Models 18.1 Prerequisites 18.2 The idea 18.3 Finding the lower ranks 18.3.1 Alternating minimization 18.3.2 Loss functions 18.3.3 Regularization 18.3.4 Selecting k 18.4 Fitting GLRMs in R 18.4.1 Basic GLRM model 18.4.2 Tuning to optimize for unseen data 18.5 Final thoughts 19. Autoencoders 19.1 Prerequisites 19.2 Undercomplete autoencoders 19.2.1 Comparing PCA to an autoencoder 19.2.2 Stacked autoencoders 19.2.3 Visualizing the reconstruction 19.3 Sparse autoencoders 19.4 Denoising autoencoders 19.5 Anomaly detection 19.6 Final thoughts IV Clustering 20. K-means Clustering 20.1 Prerequisites 20.2 Distance measures 20.3 Defining clusters 20.4 k-means algorithm 20.5 Clustering digits 20.6 How many clusters? 20.7 Clustering with mixed data 20.8 Alternative partitioning methods 20.9 Final thoughts 21. Hierarchical Clustering 21.1 Prerequisites 21.2 Hierarchical clustering algorithms 21.3 Hierarchical clustering in R 21.3.1 Agglomerative hierarchical clustering 21.3.2 Divisive hierarchical clustering 21.4 Determining optimal clusters 21.5 Working with dendrograms 21.6 Final thoughts 22. Model-based Clustering 22.1 Prerequisites 22.2 Measuring probability and uncertainty 22.3 Covariance types 22.4 Model selection 22.5 My basket example 22.6 Final thoughts Bibliography Index
£78.84
Taylor & Francis Ltd Teaching Data Analytics
Book SynopsisThe need for analytics skills is a source of the burgeoning growth in the number of analytics and decision science programs in higher education developed to feed the need for capable employees in this area. The very size and continuing growth of this need means that there is still space for new program development. Schools wishing to pursue business analytics programs intentionally assess the maturity level of their programs and take steps to close the gap. Teaching Data Analytics: Pedagogy and Program Design is a reference for faculty and administrators seeking direction about adding or enhancing analytics offerings at their institutions. It provides guidance by examining best practices from the perspectives of faculty and practitioners. By emphasizing the connection of data analytics to organizational success, it reviews the position of analytics and decision science programs in higher education, and to review the critical connection between this area of study and caTable of ContentsPreface: Teaching Data Analytics—A Primer for Higher EducationAcknowledgmentsEditorsContributorsSection I Industry PerspectiveChapter 1 It’s Not All About the MathDOUG COGSWELL, ERIC CHO, AND MATEO MOLINA CORDEROChapter 2 A Two-Day Course Outline for Teaching Analytics to Fundraising Professionals: Lessons for AcademiaMARIANNE M. PELLETIERChapter 3 Developing Professional Skills in a Data Analytics ClassroomKATHRYN S. BERKOWSection II Curricular and CocurricularAssignment DesignChapter 4 Formative and Summative Assessments in Teaching Association RulesMATT NORTHChapter 5 The Necessity of Teaching Computer Simulation within Data Analytics ProgramsVIRGINIA M. MIORIChapter 6 Using Games to Create a Common Experience for StudentsSTEPHEN PENNChapter 7 Student Competitions: Extending Student Experience Outside of the ClassroomYELENA BYTENSKAYA, KATHERINE LEAMING GOLDBERG, AND ELENA GORTCHEVASection III Program Design TacticsChapter 8 Competencies for the Design, Implementation, and Adoption of the Analytics ProcessEDUARDO RODRIGUEZ, JOHN S. EDWARDS, AND GERMÁN A. RAMÍREZChapter 9 Business Analytics: A Course DesignKATHERINE LEAMING GOLDBERGChapter 10 Building a Ranked Data Analytics ProgramVIRGINIA M. MIORI, NICOLLE T. CLEMENTS, AND KATHLEEN CAMPBELL-GARWOODIndex
£142.50
Cambridge University Press theshapleyvalue
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£37.99
Cambridge University Press Essays in Panel Data Econometrics
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£45.59
Cambridge University Press Economic Complexity Proceedings of the Fourth International Symposium in Economic Theory and Econometrics 4 International Symposia in Economic Theory and Econometrics Series Number 4
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Cambridge University Press Dynamic Econometric Modelling Proceedings of the Third International Symposium in Economic Theory and Econometrics 3 International Symposia in Economic Theory and Econometrics Series Number 3
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Cambridge University Press On Concepts and Measures of Multifactor Productivity in Canada 1961 1980
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Cambridge University Press Social Choice and Welfare Proceedings of the Eighth International Symposium in Economic Theory and Econometrics 8 International Symposia in Economic Theory and Econometrics Series Number 8
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Cambridge University Press Axiomatic Theory of Bargaining with a Variable Number of Agents
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Cambridge University Press Nonlinr Economet Model Time Sr Anl Proceedings of the Eleventh International Symposium in Economic Theory 11 International Symposia in Economic Theory and Econometrics Series Number 11
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Cambridge University Press Probability Econometrics and Truth The Methodology of Econometrics
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Cambridge University Press Introduction to Econophysics Correlations and Complexity in Finance
Book SynopsisThis book concerns the use of concepts from statistical physics in the description of financial systems. The authors illustrate the scaling concepts used in probability theory, critical phenomena, and fully developed turbulent fluids. These concepts are then applied to financial time series. The authors also present a stochastic model that displays several of the statistical properties observed in empirical data. Statistical physics concepts such as stochastic dynamics, short- and long-range correlations, self-similarity and scaling permit an understanding of the global behaviour of economic systems without first having to work out a detailed microscopic description of the system. Physicists will find the application of statistical physics concepts to economic systems interesting. Economists and workers in the financial world will find useful the presentation of empirical analysis methods and well-formulated theoretical tools that might help describe systems composed of a huge number oTrade Review'… they have been remarkably successful in presenting a clear and concise introductory summary of a large body of work on the statistical properties of stock prices.' Burton Malkiel, Journal of Economic Literature'Clearly and concisely written, this book provides an excellent introduction to the problem of understanding the empirical statistical properties of prices.' Doyne Farmer, Prediction Company, Santa Fe and the Santa Fe Institute'I feel the book is a useful introduction to the empirical aspects of econophysics.' Blake LeBaron, Nature'The authors are leading researchers in the field, and were well-regarded statistical physicists before that … the book seems aimed the other way, at physicists interested in economics, and for them it would make a good introduction to finance. The writing is clear and friendly, the production values high and the guides to further reading excellent. They will find it well worth their time and money.' Cosma Shalizi, Institute of PhysicsTable of ContentsPreface; 1. Introduction; 2. Efficient market hypothesis; 3. Random walk; 4. Lévy stochastic processes and limit theorems; 5. Scales in financial data; 6. Stationarity and time correlation; 7. Time correlation in financial time series; 8. Stochastic models of price dynamics; 9. Scaling and its breakdown; 10. ARCH and GARCH processes; 11. Financial markets and turbulence; 12. Correlation and anti-correlation between stocks; 13. Taxonomy of a stock portfolio; 14. Options in idealized markets; 15. Options in real markets; Appendix A: notation guide; Appendix B: martingales; References; Index.
£39.89
Cambridge University Press The Role of Social Capital in Development
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£41.79
Cambridge University Press Conceptual Anomalies in Economics and Statistics
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£40.84
Cambridge University Press The Working of Econometric Models
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£104.50
Cambridge University Press Simulationbased Inference in Econometrics
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£39.89
Cambridge University Press Commerce Complexity and Evolution
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£46.54
Cambridge University Press Equilibrium Theory and Applications
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£39.89
Cambridge University Press Analysis Without Measurement
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£29.44
Cambridge University Press UK Tax Policy and Applied General Equilibrium Analysis
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£35.14
Cambridge University Press Reconciliation of National Income and Expenditure Balanced Estimates of National Income for the United Kingdom 19201990 7 Studies in the National and Expenditure of the UK Series Number 7
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£32.29
Cambridge University Press Simplicity Inference and Modelling Keeping it Sophisticatedly Simple
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Cambridge University Press Economic Foundations of Symmetric Programming
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£42.41
Cambridge University Press Statistical Games and Human Affairs
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Cambridge University Press The Working of Econometric Models
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Cambridge University Press Analysis of Panels and Limited Dependent Variable Models
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Cambridge University Press Weather Derivative Valuation
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Cambridge University Press Identification and Inference for Econometric Models
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Cambridge University Press Nonlinear Statistical Modeling Proceedings of the Thirteenth International Symposium in Economic Theory and Econometrics Essays in Honor of Takeshi Symposia in Economic Theory and Econometrics
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£53.19
Cambridge University Press Dynamic Disequilibrium Modeling Proceedings of the Ninth International Symposium in Economic Theory and Econometrics 9 International Symposia in Economic Theory and Econometrics Series Number 9
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£44.64
Cambridge University Press Discrete Models of Financial Markets Mastering Mathematical Finance
Book SynopsisThis book explains in simple settings the fundamental ideas of financial market modelling and derivative pricing, using the no-arbitrage principle. Relatively elementary mathematics leads to powerful notions and techniques - such as viability, completeness, self-financing and replicating strategies, arbitrage and equivalent martingale measures - which are directly applicable in practice. The general methods are applied in detail to pricing and hedging European and American options within the CoxâRossâRubinstein (CRR) binomial tree model. A simple approach to discrete interest rate models is included, which, though elementary, has some novel features. All proofs are written in a user-friendly manner, with each step carefully explained and following a natural flow of thought. In this way the student learns how to tackle new problems.Trade Review'The book could be used by a broad range of practitioners, such as analysts, risk managers, quants, consultants, and auditors in financial markets, as it provides an overview of all the basic terminologies and concepts of financial models.' Thomas S. Y. Ho, SIAM Review'… clearly written … The exposition is of well-known material, using the classical notation, and plenty of exercises for the reader are integrated into the text.' George Matthews, Mathematics TodayTable of ContentsPreface; 1. Introduction; 2. Single-step asset pricing models; 3. Multi-step binomial model; 4. Multi-step general models; 5. American options; 6. Modelling bonds and interest rates; Index.
£37.37
Cambridge University Press Game Theory
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Cambridge University Press Applications of Differential Geometry to Econometrics
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Cambridge University Press Econometric Theory and Practice
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Cambridge University Press The Capital Asset Pricing Model in the 21st Century
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Cambridge University Press The Structural Econometric Time Series Analysis Approach
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Cambridge University Press Logit Models from Economics and Other Fields
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Cambridge University Press An Elementary Introduction to Mathematical Finance
Book SynopsisThis textbook on the basics of option pricing is accessible to readers with limited mathematical training. It is for both professional traders and undergraduates studying the basics of finance. This third edition includes three new chapters, along with expanded sets of exercises and references for all the chapters.Trade Review'… an excellent introduction to the subject … the book is ideally suited for self-study and provides a very accessible entry point to this fascinating field.' ISI Short Book Reviews'… this excellent text achieves its aim to provide a highly accessible and at the same time accurate presentation of the subject. I would recommend it.' The Statistician'… an excellent introduction to the mathematics of finance … very useful as a text for an introductory course.' Zentralblatt Math'… provides an accessible and relatively deep insight into basic and advanced topics of mathematical finance … The lucid style of the exposition will be appreciated by readers interested in the topic, and by researchers, students, and practitioners.' European Maths Society JournalTable of Contents1. Probability; 2. Normal random variables; 3. Geometric Brownian motion; 4. Interest rates and present value analysis; 5. Pricing contracts via arbitrage; 6. The Arbitrage Theorem; 7. The Black–Scholes formula; 8. Additional results on options; 9. Valuing by expected utility; 10. Stochastic order relations; 11. Optimization models; 12. Stochastic dynamic programming; 13. Exotic options; 14. Beyond geometric motion models; 15. Autoregressive models and mean reversion.
£55.09
Cambridge University Press Economic Foundations of Symmetric Programming
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£81.70