Modeling count data / Joseph M. Hilbe.
Material type:
TextPublication details: New York : Cambridge University Press, 2014.Description: xv, 283 p : illustrations ; 25 cmISBN: - 9781107611252 (paperback)
- 000SA.07 23 H641
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 000SA.07 H641 (Browse shelf(Opens below)) | Available | 136177 |
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| 000SA.07 H112 Multivariate general linear models / | 000SA.07 H264 Applied multivariate statistical analysis / | 000SA.07 H286 Essence of multivariate thinking : basic themes and methods / | 000SA.07 H641 Modeling count data / | 000SA.07 H873 Theoretical foundations of functional data analysis, with an introduction to linear operators / | 000SA.07 H972 Exploratory multivariate analysis by example using R / | 000SA.07 H991 Generalized structured component analysis : |
Includes bibliographical references (pages 269-275) and index.
chapter 1. Varieties of count data --
chapter 2. Poisson regression --
chapter 3. Testing overdispersion --
chapter 4. Assessment of fit --
chapter 5. Negative binomial regression --
chapter 6. Poisson inverse Gaussian regression --
chapter 7. Problems with zeros --
chapter 8. Modeling underdispersed count data: generalized Poisson --
chapter 9. Complex data: more advanced models --
Appendix: SAS code--
Bibliography--
Index.
This entry-level text offers clear and concise guidelines on how to select, construct, interpret, and evaluate count data. Written for researchers with little or no background in advanced statistics, the book presents treatments of all major models using numerous tables, insets, and detailed modeling suggestions. It begins by demonstrating the fundamentals of linear regression and works up to an analysis of the Poisson and negative binomial models, and to the problem of overdispersion. Examples in Stata, R, and SAS code enable readers to adapt models for their own purposes, making the text an ideal resource for researchers working in public health, ecology, econometrics, transportation, and other related fields.
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