Julian J. Faraway's Books

Extending the Linear Model with R

Generalized Linear, Mixed Effects and Nonparametric Regression Models

By: Julian J. Faraway

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Extending the Linear Model with R

Generalized Linear, Mixed Effects and Nonparametric Regression Models

By: Julian J. Faraway

Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway's critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author's treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the data described in the book is available at http://people.bath.ac.uk/jjf23/ELM/ Statisticians need to be familiar with a broad range of ideas and techniques. This book provides a well-stocked toolbox of methodologies, and with its unique presentation of these very modern statistical techniques, holds the potential to break new ground in the way graduate-level courses in this area are taught.

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307

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CRC Press

Published Date

ISBN 10

0203492285

ISBN 13

9780203492284

Linear Models with Python

By: Julian J. Faraway

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Linear Models with Python

By: Julian J. Faraway

Praise for Linear Models with R: This book is a must-have tool for anyone interested in understanding and applying linear models. The logical ordering of the chapters is well thought out and portrays Faraway’s wealth of experience in teaching and using linear models. ... It lays down the material in a logical and intricate manner and makes linear modeling appealing to researchers from virtually all fields of study. -Biometrical Journal Throughout, it gives plenty of insight ... with comments that even the seasoned practitioner will appreciate. Interspersed with R code and the output that it produces one can find many little gems of what I think is sound statistical advice, well epitomized with the examples chosen...I read it with delight and think that the same will be true with anyone who is engaged in the use or teaching of linear models. -Journal of the Royal Statistical Society Like its widely praised, best-selling companion version, Linear Models with R, this book replaces R with Python to seamlessly give a coherent exposition of the practice of linear modeling. Linear Models with Python offers up-to-date insight on essential data analysis topics, from estimation, inference and prediction to missing data, factorial models and block designs. Numerous examples illustrate how to apply the different methods using Python. Features: Python is a powerful, open source programming language increasingly being used in data science, machine learning and computer science. Python and R are similar, but R was designed for statistics, while Python is multi-talented. This version replaces R with Python to make it accessible to a greater number of users outside of statistics, including those from Machine Learning. A reader coming to this book from an ML background will learn new statistical perspectives on learning from data. Topics include Model Selection, Shrinkage, Experiments with Blocks and Missing Data. Includes an Appendix on Python for beginners. Linear Models with Python explains how to use linear models in physical science, engineering, social science and business applications. It is ideal as a textbook for linear models or linear regression courses.

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315

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CRC Press

Published Date

ISBN 10

1351053396

ISBN 13

9781351053396

Bayesian Regression Modeling with INLA

By: Xiaofeng Wang,Yu Ryan Yue,Julian J. Faraway

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Bayesian Regression Modeling with INLA

By: Xiaofeng Wang,Yu Ryan Yue,Julian J. Faraway

INLA stands for Integrated Nested Laplace Approximations, which is a new method for fitting a broad class of Bayesian regression models. No samples of the posterior marginal distributions need to be drawn using INLA, so it is a computationally convenient alternative to Markov chain Monte Carlo (MCMC), the standard tool for Bayesian inference. Bayesian Regression Modeling with INLA covers a wide range of modern regression models and focuses on the INLA technique for building Bayesian models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to demonstrate the interplay of theory and practice with reproducible studies. Complete R commands are provided for each example, and a supporting website holds all of the data described in the book. An R package including the data and additional functions in the book is available to download. The book is aimed at readers who have a basic knowledge of statistical theory and Bayesian methodology. It gets readers up to date on the latest in Bayesian inference using INLA and prepares them for sophisticated, real-world work. Xiaofeng Wang is Professor of Medicine and Biostatistics at the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University and a Full Staff in the Department of Quantitative Health Sciences at Cleveland Clinic. Yu Ryan Yue is Associate Professor of Statistics in the Paul H. Chook Department of Information Systems and Statistics at Baruch College, The City University of New York. Julian J. Faraway is Professor of Statistics in the Department of Mathematical Sciences at the University of Bath.

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304

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CRC Press

Published Date

ISBN 10

1351165747

ISBN 13

9781351165747

Linear Models with R

By: Julian J. Faraway

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Linear Models with R

By: Julian J. Faraway

A Hands-On Way to Learning Data AnalysisPart of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models

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284

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CRC Press

Published Date

ISBN 10

1439887349

ISBN 13

9781439887349

Linear Models with Python

By: Julian J. Faraway

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Linear Models with Python

By: Julian J. Faraway

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309

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CRC Press

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ISBN 10

135105340X

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9781351053402

Extending the Linear Model with R

Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition

By: Julian J. Faraway

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Extending the Linear Model with R

Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition

By: Julian J. Faraway

Start Analyzing a Wide Range of Problems Since the publication of the bestselling, highly recommended first edition, R has considerably expanded both in popularity and in the number of packages available. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition takes advantage of the greater functionality now available in R and substantially revises and adds several topics. New to the Second Edition Expanded coverage of binary and binomial responses, including proportion responses, quasibinomial and beta regression, and applied considerations regarding these models New sections on Poisson models with dispersion, zero inflated count models, linear discriminant analysis, and sandwich and robust estimation for generalized linear models (GLMs) Revised chapters on random effects and repeated measures that reflect changes in the lme4 package and show how to perform hypothesis testing for the models using other methods New chapter on the Bayesian analysis of mixed effect models that illustrates the use of STAN and presents the approximation method of INLA Revised chapter on generalized linear mixed models to reflect the much richer choice of fitting software now available Updated coverage of splines and confidence bands in the chapter on nonparametric regression New material on random forests for regression and classification Revamped R code throughout, particularly the many plots using the ggplot2 package Revised and expanded exercises with solutions now included Demonstrates the Interplay of Theory and Practice This textbook continues to cover a range of techniques that grow from the linear regression model. It presents three extensions to the linear framework: GLMs, mixed effect models, and nonparametric regression models. The book explains data analysis using real examples and includes all the R commands necessary to reproduce the analyses.

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399

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CRC Press

Published Date

ISBN 10

1498720986

ISBN 13

9781498720984

Extending the Linear Model with R

Generalized Linear, Mixed Effects and Nonparametric Regression Models

By: Julian J. Faraway

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Extending the Linear Model with R

Generalized Linear, Mixed Effects and Nonparametric Regression Models

By: Julian J. Faraway

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413

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Chapman & Hall/CRC Texts in Statistical Science

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ISBN 10

149872096X

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9781498720960

Extending the Linear Model with R

Generalized Linear, Mixed Effects and Nonparametric Regression Models

By: Julian J. Faraway

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Extending the Linear Model with R

Generalized Linear, Mixed Effects and Nonparametric Regression Models

By: Julian J. Faraway

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Solve jigsaw puzzle

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Page Count

307

Publisher

CRC Press

Published Date

ISBN 10

0203492285

ISBN 13

9780203492284

Book's by Julian J. Faraway

Extending the Linear Model with R

Extending the Linear Model with R

Linear Models with Python

Linear Models with Python

Bayesian Regression Modeling with INLA

Bayesian Regression Modeling with INLA

Linear Models with R

Linear Models with R

Linear Models with Python

Linear Models with Python

Extending the Linear Model with R

Extending the Linear Model with R

Extending the Linear Model with R

Extending the Linear Model with R

Extending the Linear Model with R

Extending the Linear Model with R

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