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Negative binomial regression /
"This second edition of Hilbe's Negative Binomial Regression is a substantial enhancement to the popular first edition. The only text devoted entirely to the negative binomial model and its many variations, nearly every model discussed in the literature is addressed. The theoretical and di...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge, UK ; New York :
Cambridge University Press,
2011.
|
| Edición: | Second edition. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Hilbe, Joseph M., |d 1944- |e author. | |
| 245 | 1 | 0 | |a Negative binomial regression / |c Joseph M. Hilbe. |
| 250 | |a Second edition. | ||
| 264 | 1 | |a Cambridge, UK ; |a New York : |b Cambridge University Press, |c 2011. | |
| 300 | |a 1 online resource (xviii, 553 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 347 | |a data file | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 520 | |a "This second edition of Hilbe's Negative Binomial Regression is a substantial enhancement to the popular first edition. The only text devoted entirely to the negative binomial model and its many variations, nearly every model discussed in the literature is addressed. The theoretical and distributional background of each model is discussed, together with examples of their construction, application, interpretation, and evaluation. Complete Stata and R code are provided throughout the text, with additional code (plus SAS), derivations, and data provided on the book's website. Written for the practicing researcher, the text begins with an examination of risk and rate ratios, and of the estimating algorithms used to model count data. The book then gives an in-depth analysis of Poisson regression and an evaluation of the meaning and nature of overdispersion, followed by a comprehensive analysis of the negative binomial distribution and of its parameterizations into various models for evaluating count data"-- |c Provided by publisher | ||
| 505 | 0 | |a The concept of risk -- Overview of count response models -- Methods of estimation -- Assessment of count models -- Poisson regression -- Overdispersion -- Negative binomial regression -- Negative binomial regression: modeling -- Alternative variance parameterizations -- Problems with zero counts -- Censored and truncated count models -- Handling endogeneity and latent class models -- Count panel models -- Bayesian negative binomial models -- Appendix A. Constructing and interpreting interactions terms -- Appendix B. Data sets, commands, functions. | |
| 546 | |a English. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Negative binomial distribution. | |
| 650 | 0 | |a Poisson algebras. | |
| 650 | 4 | |a Mathematics. | |
| 650 | 6 | |a Loi binomiale négative. | |
| 650 | 6 | |a Algèbres de Poisson. | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a Negative binomial distribution. |2 fast |0 (OCoLC)fst01035488 | |
| 650 | 7 | |a Poisson algebras. |2 fast |0 (OCoLC)fst01068204 | |
| 650 | 7 | |a Statistisk metod. |2 sao | |
| 650 | 7 | |a Distributionsteori. |2 sao | |
| 776 | 0 | 8 | |i Print version: |a Hilbe, Joseph. |t Negative binomial regression. |b 2nd ed. |d Cambridge ; New York : Cambridge University Press, 2011 |z 9780521198158 |w (DLC) 2010051121 |w (OCoLC)694679188 |
| 856 | 4 | 0 | |u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=357443 |z Texto completo |
| 880 | 0 | |6 505-00/(S |a Cover -- Negative Binomial Regression -- Title -- Copyright -- Contents -- Preface to the second edition -- New subjects discussed in the second edition -- To those who made this a better text -- 1 Introduction -- 1.1 What is a negative binomial model-- 1.2 A brief history of the negative binomial -- 1.3 Overview of the book -- 2 The concept of risk -- 2.1 Risk and 2×2 tables -- 2.2 Risk and 2×k tables -- 2.3 Risk ratio confidence intervals -- 2.4 Risk difference -- 2.5 The relationship of risk to odds ratios -- 2.6 Marginal probabilities: joint and conditional -- Summary -- 3 Overview of count response models -- 3.1 Varieties of count response model -- 3.2 Estimation -- 3.3 Fit considerations -- Summary -- 4 Methods of estimation -- 4.1 Derivation of the IRLS algorithm -- 4.1.1 Solving for ∂L or U -- the gradient -- 4.1.2 Solving for ∂2 L -- 4.1.3 The IRLS fitting algorithm -- 4.2 Newton-Raphson algorithms -- 4.2.1 Derivation of the Newton-Raphson -- 4.2.2 GLM with OIM -- 4.2.3 Parameterizing from μ to x'β -- 4.2.4 Maximum likelihood estimators -- Summary -- 5 Assessment of count models -- 5.1 Residuals for count response models -- 5.2 Model fit tests -- 5.2.1 Traditional fit tests -- 5.2.1.1 R2 and pseudo-R2 Goodness-of-fit tests -- 5.2.1.2 Deviance goodness-of-fit test -- 5.2.1.4 Likelihood-ratio test -- 5.2.2 Information criteria fit tests -- 5.2.2.1 Akaike Information Criterion -- 5.2.2.2 Bayesian Information Criterion -- 5.3 Validation models -- Summary -- 6 Poisson regression -- 6.1 Derivation of the Poisson model -- 6.1.1 Derivation of the Poisson from the binomial distribution -- 6.1.2 Derivation of the Poisson model -- 6.2 Synthetic Poisson models -- 6.2.1 Construction of synthetic models -- 6.2.2 Changing response and predictor values -- Changes to the response -- Changes to the predictor. | |
| 880 | 8 | |6 505-00/(S |a 8.4.2 NB2: expected information matrix -- 8.4.3 NB2: observed information matrix -- 8.4.4 NB2: R maximum likelihood function -- Summary -- 9 Negative binomial regression: modeling -- 9.1 Poisson versus negative binomial -- 9.2 Synthetic negative binomial -- 9.3 Marginal effects and discrete change -- 9.4 Binomial versus count models -- 9.5 Examples: negative binomial regression -- Example 1: Modeling number of marital affairs -- Example 2: Heart procedures -- Example 3: Titanic survival data -- Example 4: Health reform data -- Summary -- 10 Alternative variance parameterizations -- 10.1 Geometric regression: NB α = 1 -- 10.1.1 Derivation of the geometric -- 10.1.2 Synthetic geometric models -- 10.1.3 Using the geometric model -- 10.1.4 The canonical geometric model -- 10.2 NB1: The linear negative binomial model -- 10.2.1 NB1 as QL-Poisson -- 10.2.2 Derivation of NB1 -- 10.2.3 Modeling with NB1 -- 10.2.4 NB1: R maximum likelihood function -- 10.3 NB-C: Canonical negative binomial regression -- 10.3.1 NB-C overview and formulae -- 10.3.2 Synthetic NB-C models -- 10.3.3 NB-C models -- 10.4 NB-H: Heterogeneous negative binomial regression -- 10.5 The NB-P model: generalized negative binomial -- 10.6 Generalized Waring regression -- 10.7 Bivariate negative binomial -- 10.8 Generalized Poisson regression -- 10.9 Poisson inverse Gaussian regression (PIG) -- 10.10 Other count models -- Summary -- 11 Problems with zero counts -- 11.1 Zero-truncated count models -- 11.2 Hurdle models -- 11.2.1 Theory and formulae for hurdle models -- 11.2.2 Synthetic hurdle models -- 11.2.3 Applications -- 11.2.4 Marginal effects -- 11.3 Zero-inflated negative binomial models -- 11.3.1 Overview of ZIP/ZINB models -- 11.3.2 ZINB algorithms -- 11.3.3 Applications -- 11.3.4 Zero-altered negative binomial -- 11.3.5 Tests of comparative fit -- 11.3.6 ZINB marginal effects. | |
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