Chapman and Hall/Crc Texts in Statistical Science Ser.: Extending the Linear...

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Características del artículo

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Como nuevo: Libro en perfecto estado y poco leído. La tapa no tiene desperfectos y si procede, con ...
ISBN
9781584884248
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Product Identifiers

Publisher
CRC Press LLC
ISBN-10
158488424X
ISBN-13
9781584884248
eBay Product ID (ePID)
9038424459

Product Key Features

Number of Pages
312 Pages
Publication Name
Extending the Linear Model with R : Generalized Linear, Mixed Effects and Nonparametric Regression Models
Language
English
Subject
Probability & Statistics / General
Publication Year
2005
Type
Textbook
Subject Area
Mathematics
Author
Julian J. Faraway
Series
Chapman and Hall/Crc Texts in Statistical Science Ser.
Format
Hardcover

Dimensions

Item Height
0.9 in
Item Weight
20 Oz
Item Length
9.5 in
Item Width
6.2 in

Additional Product Features

Intended Audience
College Audience
LCCN
2005-054822
Reviews
"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 "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, Imperial College, Journal of Applied Statistics, July 2007, Vol. 34, No. 5 ". . . 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, Uppsala University, 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, Clemson University, Journal of the American Statistical Association, December 2007, Vol. 102, No. 480 "It provides a well-stocked toolbook of methodologies, and with its unique presentation on these very modern statistical techniques, holds the potential to break new ground in the way graduate-level courses in this area are taught." -János Sztrik, Zentralblatt Math, 2006, Vol. 1095, No. 21, "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 e In summary, this is book is highly recommendede" -Biometrics, December 2006 "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." e"Giovanni Montana, Imperial College, Journal of Applied Statistics, July 2007, Vol. 34, No. 5 ". . . 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." e"Andreas Rosenblad, Uppsala University, 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." e"Colin Gallagher, Clemson University, Journal of the American Statistical Association, December 2007, Vol. 102, No. 480 "It provides a well-stocked toolbook of methodologies, and with its unique presentation on these very modern statistical techniques, holds the potential to break new ground in the way graduate-level courses in this area are taught." e"Jnos Sztrik, Zentralblatt Math, 2006, Vol. 1095, No. 21, "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 "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, Imperial College, Journal of Applied Statistics, July 2007, Vol. 34, No. 5 ". . . 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, Uppsala University, 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, Clemson University, Journal of the American Statistical Association, December 2007, Vol. 102, No. 480 "It provides a well-stocked toolbook of methodologies, and with its unique presentation on these very modern statistical techniques, holds the potential to break new ground in the way graduate-level courses in this area are taught." -Jnos Sztrik, Zentralblatt Math, 2006, Vol. 1095, No. 21
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
519.5/38
Table Of Content
INTRODUCTION BINOMIAL DATA Challenger Disaster Example Binomial Regression Model Inference Tolerance Distribution Interpreting Odds Prospective and Retrospective Sampling Choice of Link Function Estimation Problems Goodness of Fit Prediction and Effective Doses Overdispersion Matched Case-Control Studies COUNT REGRESSION Poisson Regression Rate Models Negative Binomial CONTINGENCY TABLES Two-by-Two Tables Larger Two-Way Tables Matched Pairs Three-Way Contingency Tables Ordinal Variables MULTINOMIAL DATA Multinomial Logit Model Hierarchical or Nested Responses Ordinal Multinomial Responses GENERALIZED LINEAR MODELS GLM Definition Fitting a GLM Hypothesis Tests GLM Diagnostics OTHER GLMS Gamma GLM Inverse Gaussian GLM Joint Modeling of the Mean and Dispersion Quasi-Likelihood RANDOM EFFECTS Estimation Inference Predicting Random Effects 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 MIXED EFFECT MODELS FOR NONNORMAL RESPONSES Generalized Linear Mixed Models Generalized Estimating Equations NONPARAMETRIC REGRESSION Kernel Estimators Splines Local Polynomials Wavelets Other Methods Comparison of Methods Multivariate Predictors ADDITIVE MODELS Additive Models Using the gam Package 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 Classification Trees NEURAL NETWORKS Statistical Models as NNs Feed-Forward Neural Network with One Hidden Layer NN Application Conclusion APPENDICES Likelihood Theory R Information Bibliography Index
Synopsis
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., 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 Rexamined 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 Rsurveys 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., Linear models form the foundation of a vast range of statistical methodologies. Julian J. Faraway's critically acclaimed Linear Models with R thoroughly examined the different methods available, and showed in which situations each one applies. Following in those footsteps, his new book surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models, mixed effect models, and nonparametric regression models. It 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.
LC Classification Number
QA279.F368 2006

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