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Chapman and Hall/CRC Texts in Statistical Science Ser.: Extension du linéaire...
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Numéro de l'objet eBay :266541274051
Caractéristiques de l'objet
- État
- Comme neuf
- Commentaires du vendeur
- “No marks in the book. Looks brand new”
- Subject Area
- Mathematics
- Country/Region of Manufacture
- United States
- Subject
- Probability & Statistics / General, Probability & Statistics / Regression Analysis
- ISBN
- 9781498720960
- Publication Name
- Extending the Linear Model with R : Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition
- Publisher
- CRC Press LLC
- Item Length
- 9.5 in
- Publication Year
- 2016
- Series
- Chapman and Hall/Crc Texts in Statistical Science Ser.
- Type
- Textbook
- Format
- Mixed Lot
- Language
- English
- Item Height
- 1 in
- Item Weight
- 24.1 Oz
- Item Width
- 6.4 in
- Number of Pages
- 399 Pages
À propos de ce produit
Product Identifiers
Publisher
CRC Press LLC
ISBN-10
149872096X
ISBN-13
9781498720960
eBay Product ID (ePID)
20038566203
Product Key Features
Number of Pages
399 Pages
Publication Name
Extending the Linear Model with R : Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition
Language
English
Subject
Probability & Statistics / General, Probability & Statistics / Regression Analysis
Publication Year
2016
Type
Textbook
Subject Area
Mathematics
Series
Chapman and Hall/Crc Texts in Statistical Science Ser.
Format
Mixed Lot
Dimensions
Item Height
1 in
Item Weight
24.1 Oz
Item Length
9.5 in
Item Width
6.4 in
Additional Product Features
Edition Number
2
Intended Audience
College Audience
LCCN
2015-045130
Reviews
Praise for the First Edition: "... well-written and the discussions are easy to follow ... very useful as a reference book for applied statisticians and would also serve well as a textbook for students graduating in statistics." --Computational Statistics, April 2009, Vol. 24 "The text is well organized and carefully written ... provides an overview of many modern statistical methodologies and their applications to real data using software. This makes it a useful text for practitioners and graduate students alike." --Journal of the American Statistical Association, December 2007, Vol. 102, No. 480 "I enjoyed this text as much as [Faraway's Linear Models with R ]. The book is recommended as a textbook for a computational statistical and data mining course including GLMs and non-parametric regression, and will also be of great value to the applied statistician whose statistical programming environment of choice is R." --Journal of Applied Statistics, July 2007, Vol. 34, No. 5 "This is a very pleasant book to read. It clearly demonstrates the different methods available and in which situations each one applies. It covers almost all of the standard topics beyond linear models that a graduate student in statistics should know. It also includes discussion of topics such as model diagnostics, rarely addressed in books of this type. The presentation incorporates an abundance of well-chosen examples ... this book is highly recommended ..." --Biometrics, December 2006, "What I liked most with this book was the comprehensive treatment of the practical application of GLMs, covering most outcomes an applied statistician will encounter, and at the same time presenting just enough of the necessary theoretical basis for the discussed methods. Combined with the thorough discussion of the R output, the text will serve as a useful guide for the reader when applying the methods to his or her own data set." --Psychometrika, 2018 "The second edition of book 'Extending the linear model with R' by Julian Faraway is an easily readable and relatively thorough (without being theory heavy) sequel of the earlier 'Linear Models with R' by the same author. The book itself is written in a self-paced tutorial style in easily digestible chunks integrating descriptions of underlying methodology, with data analysis and R code. The organization of the book is well thought through. The flow of the book is problem driven rather than driven by the underlying statistical theory . . . the second edition is more polished in terms of the figures used, R code and output display and a crisper typesetting of equations." --John T. Ormerod, University of Sydney Praise for the First Edition: "... well-written and the discussions are easy to follow ... very useful as a reference book for applied statisticians and would also serve well as a textbook for students graduating in statistics." --Computational Statistics, April 2009, Vol. 24 "The text is well organized and carefully written ... provides an overview of many modern statistical methodologies and their applications to real data using software. This makes it a useful text for practitioners and graduate students alike." --Journal of the American Statistical Association, December 2007, Vol. 102, No. 480 "I enjoyed this text as much as [Faraway's Linear Models with R ]. The book is recommended as a textbook for a computational statistical and data mining course including GLMs and non-parametric regression, and will also be of great value to the applied statistician whose statistical programming environment of choice is R." --Journal of Applied Statistics, July 2007, Vol. 34, No. 5 "This is a very pleasant book to read. It clearly demonstrates the different methods available and in which situations each one applies. It covers almost all of the standard topics beyond linear models that a graduate student in statistics should know. It also includes discussion of topics such as model diagnostics, rarely addressed in books of this type. The presentation incorporates an abundance of well-chosen examples ... this book is highly recommended ..." --Biometrics, December 2006 "It has been a great pleasure to review this book, which delivers both a readily accessible and reader-friendly account of a wide range of statistical models in the context of R software. Since the publication of the very well received first edition of the book, R has considerably expanded both in popularity and in the number of packages available. The second editionof the book takes advantage of the greater functionality available now in R, and substantially revises and adds several new topics." --Andrzej Galecki, The International Biometric Society, Praise for the First Edition: ". . . well-written and the discussions are easy to follow . . . very useful as a reference book for applied statisticians and would also serve well as a textbook for students graduating in statistics." -Andreas Rosenblad, Computational Statistics, April 2009, Vol. 24 "The text is well organized and carefully written . . . provides an overview of many modern statistical methodologies and their applications to real data using software. This makes it a useful text for practitioners and graduate students alike." -Colin Gallagher, Journal of the American Statistical Association, December 2007, Vol. 102, No. 480 "I enjoyed this text as much as the first one. The book is recommended as a textbook for a computational statistical and data mining course including GLMs and non-parametric regression, and will also be of great value to the applied statistician whose statistical programming environment of choice is R." -Giovanni Montana, Journal of Applied Statistics, July 2007, Vol. 34, No. 5 "This is a very pleasant book to read. It clearly demonstrates the different methods available and in which situations each one applies. It covers almost all of the standard topics beyond linear models that a graduate student in statistics should know. It also includes discussion of topics such as model diagnostics, rarely addressed in books of this type. The presentation incorporates an abundance of well-chosen examples ... In summary, this is book is highly recommended..." -Biometrics, December 2006
Dewey Edition
23
Series Volume Number
124
Illustrated
Yes
Dewey Decimal
519.5/38
Edition Description
Revised edition,New Edition
Table Of Content
Introduction Binary Response Heart Disease Example Logistic Regression Inference Diagnostics Model Selection Goodness of Fit Estimation Problems Binomial and Proportion Responses Binomial Regression Model Inference Pearson's χ 2 Statistic Overdispersion Quasi-Binomial Beta Regression Variations on Logistic Regression Latent Variables Link Functions Prospective and Retrospective Sampling Prediction and Effective Doses Matched Case-Control Studies Count Regression Poisson Regression Dispersed Poisson Model Rate Models Negative Binomial Zero Inflated Count Models Contingency Tables Two-by-Two Tables Larger Two-Way Tables Correspondence Analysis Matched Pairs Three-Way Contingency Tables Ordinal Variables Multinomial Data Multinomial Logit Model Linear Discriminant Analysis Hierarchical or Nested Responses Ordinal Multinomial Responses Generalized Linear Models GLM Definition Fitting a GLM Hypothesis Tests GLM Diagnostics Sandwich Estimation Robust Estimation Other GLMs Gamma GLM Inverse Gaussian GLM Joint Modeling of the Mean and Dispersion Quasi-Likelihood GLM Tweedie GLM Random Effects Estimation Inference Estimating Random Effects Prediction Diagnostics Blocks as Random Effects Split Plots Nested Effects Crossed Effects Multilevel Models Repeated Measures and Longitudinal Data Longitudinal Data Repeated Measures Multiple Response Multilevel Models Bayesian Mixed Effect Models STAN INLA Discussion Mixed Effect Models for Nonnormal Responses Generalized Linear Mixed Models Inference Binary Response Count Response Generalized Estimating Equations Nonparametric Regression Kernel Estimators Splines Local Polynomials Confidence Bands Wavelets Discussion of Methods Multivariate Predictors Additive Models Modeling Ozone Concentration Additive Models Using mgcv Generalized Additive Models Alternating Conditional Expectations Additivity and Variance Stabilization Generalized Additive Mixed Models Multivariate Adaptive Regression Splines Trees Regression Trees Tree Pruning Random Forests Classification Trees Classification Using Forests Neural Networks Statistical Models as NNs Feed-Forward Neural Network with One Hidden Layer NN Application Conclusion Appendix A: Likelihood Theory Appendix B: About R Bibliography Index
Synopsis
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.
LC Classification Number
QA279.F368 2016
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