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Introduction to Generalized Linear Models, Third Edition 3rd New edition [Hardback]

4.00/5 (67 ratings by Goodreads)
(University of Queensland, Herston, Australia), (Queensland University of Technology, Kelvin Grove, Australia)
  • Formāts: Hardback, 320 pages, height x width: 235x156 mm, weight: 500 g, 101 Tables, black and white; 59 Illustrations, black and white
  • Sērija : Chapman & Hall/CRC Texts in Statistical Science
  • Izdošanas datums: 01-Jul-2008
  • Izdevniecība: Chapman & Hall/CRC
  • ISBN-10: 1584889500
  • ISBN-13: 9781584889502
Citas grāmatas par šo tēmu:
Introduction to Generalized Linear Models, Third Edition 3rd New editionPalielināt
  • Formāts: Hardback, 320 pages, height x width: 235x156 mm, weight: 500 g, 101 Tables, black and white; 59 Illustrations, black and white
  • Sērija : Chapman & Hall/CRC Texts in Statistical Science
  • Izdošanas datums: 01-Jul-2008
  • Izdevniecība: Chapman & Hall/CRC
  • ISBN-10: 1584889500
  • ISBN-13: 9781584889502
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781584889502.html
Keywords:
Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Models, Third Edition provides a cohesive framework for statistical modeling. This new edition of a bestseller has been updated with Stata, R, and WinBUGS code as well as three new chapters on Bayesian analysis. Like its predecessor, this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers normal, Poisson, and binomial distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian models, and Markov chain Monte Carlo (MCMC) methods.

Using popular statistical software programs, this concise and accessible text illustrates practical approaches to estimation, model fitting, and model comparisons. It includes examples and exercises with complete data sets for nearly all the models covered.

Recenzijas

Overall, this new edition remains a highly useful and compact introduction to a large number of seemingly disparate regression models. Depending on the background of the audience, it will be suitable for upper-level undergraduate or beginning post-graduate courses. Christian Kleiber, Statistical Papers (2012) 53

The comments of Lang in his review of the second edition, that `This relatively short book gives a nice introductory overview of the theory underlying generalized linear modelling. can equally be applied to the new edition. three new chapters on Bayesian analysis are also added. suitable for experienced professionals needing to refresh their knowledge . Pharmaceutical Statistics, 2011 The chapters are short and concise, and the writing is clear explanations are fundamentally sound and aimed well at an upper-level undergrad or early graduate student in a statistics-related field. This is a very worthwhile book: a good class text and a practical reference for applied statisticians.

Biometrics



This book promises in its introductory section to provide a unifying framework for many statistical techniques. It accomplishes this goal easily. Furthermore, the text covers important topics that are frequently overlooked in introductory courses, such as models for ordinal outcomes. This book is an excellent resource, either as an introduction to or a reminder of the technical aspects of generalized linear models and provides a wealth of simple yet useful examples and data sets. Journal of Biopharmaceutical Statistics, Issue 2



Praise for the Second Edition: The second edition is successful in filling a void in the otherwise sparse literature on the subject of generalized linear models at the introductory level a wide range of research applications are covered and ample workings are also provided to aid the reader in statistical calculations I would highly recommend this text . Kerrie Nelson, Statistics in Medicine, Vol. 23

Preface
1 Introduction
1
1.1 Background
1
1.2 Scope
1
1.3 Notation
5
1.4 Distributions related to the Normal distribution
7
1.5 Quadratic forms
11
1.6 Estimation
12
1.7 Exercises
15
2 Model Fitting
19
2.1 Introduction
19
2.2 Examples
19
2.3 Some principles of statistical modelling
32
2.4 Notation and coding for explanatory variables
37
2.5 Exercises
40
3 Exponential Family and Generalized Linear Models
45
3.1 Introduction
45
3.2 Exponential family of distributions
46
3.3 Properties of distributions in the exponential family
48
3.4 Generalized linear models
51
3.5 Examples
52
3.6 Exercises
55
4 Estimation
59
4.1 Introduction
59
4.2 Example: Failure times for pressure vessels
59
4.3 Maximum likelihood estimation
64
4.4 Poisson regression example
66
4.5 Exercises
69
5 Inference
73
5.1 Introduction
73
5.2 Sampling distribution for score statistics
74
5.3 Taylor series approximations
76
5.4 Sampling distribution for MLEs
77
5.5 Log-likelihood ratio statistic
79
5.6 Sampling distribution for the deviance
80
5.7 Hypothesis testing
85
5.8 Exercises
87
6 Normal Linear Models
89
6.1 Introduction
89
6.2 Basic results
89
6.3 Multiple linear regression
95
6.4 Analysis of variance
102
6.5 Analysis of covariance
114
6.6 General linear models
117
6.7 Exercises
118
7 Binary Variables and Logistic Regression
123
7.1 Probability distributions
123
7.2 Generalized linear models
124
7.3 Dose response models
124
7.4 General logistic regression model
131
7.5 Goodness of fit statistics
135
7.6 Residuals
138
7.7 Other diagnostics
139
7.8 Example: Senility and WAIS
140
7.9 Exercises
143
8 Nominal and Ordinal Logistic Regression
149
8.1 Introduction
149
8.2 Multinomial distribution
149
8.3 Nominal logistic regression
151
8.4 Ordinal logistic regression
157
8.5 General comments
199
8.6 Exercises
193
9 Poisson Regression and Log-Linear Models
165
9.1 Introduction
9.2 Poisson regression
166
9.3 Examples of conitingency tables
171
9.4 Probability models for contingency tables
175
9.5 Log-linear models
177
9 6 Inference for log-linear models
178
9.7 Numerical examples
179
9.8 Remarks
183
9.9 Exercises
183
10 Survival Analysis 187
10.1 Introduction
187
10.2 Survivor functions and hazard functions
189
10.3 Empirical survivor function
193
10.4 Estimation
195
10.5 Inference
198
10.6 Model checking
199
10.7 Example: Remission times
201
10.8 Exercises
202
11 Clustered and Longitudinal Data 207
11.1 Introduction
207
11.2 Example: Recovery from stroke
209
11.3 Repeated measures models for Normal data
213
11.4 Repeated measures models for non-Normal data
218
11.5 Multilevel models
219
11.6 Stroke example continued
222
11.7 Comments
224
11.8 Exercises
225
12 Bayesian Analysis 229
12.1 Frequentist and Bayesian paradigms
229
12.2 Priors
233
12.3 Distributions and hierarchies in Bayesian analysis
238
12.4 WinBUGS software for Bayesian analysis
238
12.5 Exercises
241
13 Markov Chain Monte Carlo Methods 243
13.1 Why standard inference fails
243
13.2 Monte Carlo integration
243
13.3 Markov chains
245
13.4 Bayesian inference
255
13.5 Diagnostics of chain convergence
256
13.6 Bayesian model fit: the DIC
260
13.7 Exercises
262
14 Example Bayesian Analyses 267
14.1 Introduction
267
14.2 Binary variables and logistic regression
267
14.3 Nominal logistic regression
271
14.4 Latent variable model
272
14.5 Survival analysis
275
14.6 Random effects
277
14.7 Longitudinal data analysis
279
14.8 Some practical tips for WinBUGS
286
14.9 Exercises
288
Appendix 291
Software 293
References 295
Index 303