Negative Binomial Regression负二项回归
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作者： Joseph M. Hilbe著
出 版 社：
出版时间： 2007-7-1字数：版次： 1页数： 251印刷时间： 2007/07/01开本： 16开印次： 1纸张： 胶版纸I S B N ： 9780521857727包装： 精装编辑推荐
For practicing researchers and statisticians who need to update their knowledge, this is the first book devoted to the negative binomial model and its many variations. Covers every model currently offered in commercial statistical software packages in detail, with numerous examples of their application and specific guidance on modeling strategy.
内容简介
At last - a book devoted to the negative binomial model and its many variations. Every model currently offered in commercial statistical software packages is discussed in detail - how each is derived, how each resolves a distributional problem, and numerous examples of their application. Many have never before been thoroughly examined in a text on count response models: the canonical negative binomial; the NB-P model, where the negative binomial exponent is itself parameterized; and negative binomial mixed models. As the models address violations of the distributional assumptions of the basic Poisson model, identifying and handling overdispersion is a unifying theme. For practising researchers and statisticians who need to update their knowledge of Poisson and negative binomial models, the book provides a comprehensive overview of estimating methods and algorithms used to model counts, as well as specific guidelines on modeling strategy and how each model can be analyzed to access goodness-of-fit.
作者简介
Joseph M. Hilbe is Emeritus Professor of Philosophy, University of Hawaii, and Adjunct Professor of Statistics, Arizona State University; Fellow, American Statistical Association; Fellow, Royal Statistical Society; Software Reviews Editor, The American Statistician (since 1997). His books include Negative Binomial Regression (2007, Cambridge University Press), Generalized Estimating Equations (with J. Hardin; 2002, CRC Press), and Generalized Linear Models and Extensions (with J. Hardin; 2001, 2007, Stata Press).
目录
Preface
Introduction
1 Overview of count response models
1.1 Varieties of count response model
1.2 Estimation
1.3 Fit considerations
1.4 Brief history of the negative binomial
1.5 Summary
2 Methods of estimation
2.1 Derivation of the IRLS algorithm
2.2 Newton–Raphson algorithms
2.3 The exponential family
2.4 Residuals for count response models
2.5 Summary
3 Poisson regression
3.1 Derivation of the Poisson model
3.2 Parameterization as a rate model
3.3 Testing overdispersion
3.4 Summary
4 Overdispersion
4.1 What is overdispersion?
4.2 Handling apparent overdispersion
4.3 Methods of handling real overdispersion
4.4 Summary
5 Negative binomial regression
5.1 Varieties of negative binomial
5.2 Derivation of the negative binomial
5.3 Negative binomial distributions
5.4 Algorithms
5.5 Summary
6 Negative binomial regression: modeling
6.1 Poisson versus negative binomial
6.2 Binomial versus count models
6.3 Examples: negative binomial regression
6.4 Summary
7 Alternative variance parameterizations
7.1 Geometric regression
7.2 NB-1: The linear constant model
7.3 NB-H: Heterogeneous negative binomial regression
7.4 The NB-P model
7.5 Generalized Poisson regression
7.6 Summary
8 Problems with zero counts
8.1 Zero-truncated negative binomial
8.2 Negative binomial with endogenous stratification
8.3 Hurdle models
8.4 Zero-inflated count models
8.5 Summary
9 Negative binomial with censoring, truncation, and sample selection
9.1 Censored and truncated models – econometric parameterization
9.2 Censored poisson and NB-2 models – survival parameterization
9.3 Sample selection models
9.4 Summary
10 Negative binomial panel models
10.1 Unconditional fixed-effects negative binomial model
10.2 Conditional fixed-effects negative binomial model
10.3 Random-effects negative binomial
10.4 Generalized estimating equation
10.5 Multilevel negative binomial models
10.6 Summary
Appendix A: Negative binomial log-likelihood functions
Appendix B: Deviance functions
Appendix C: Stata negative binomial – ML algorithm
Appendix D: Negative binomial variance functions
Appendix E: Data sets
References
Author index
Subject index
