Foundations of linear and generalized linear models / Alan Agresti.
Material type:
TextSeries: Wiley series in probability and statisticsPublication details: New Jersey : John Wiley & Sons Inc., c2015.Description: xiii, 444 p. : illustrations ; 25 cmISBN: - 9781118730034 (hardback)
- 000SA.062 23 Ag277
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 000SA.062 Ag277 (Browse shelf(Opens below)) | Available | 136469 |
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| 000SA.06 Sa163 Statistical inference for models with multivariate t-distributed errors / | 000SA.06 T136 Learning regression analysis by simulation / | 000SA.061 R873 Graphical models for categorical data / | 000SA.062 Ag277 Foundations of linear and generalized linear models / | 000SA.062 C435 Some nonparametric hybrid predictive models: asymptotic properties and applications/ | 000SA.062 C554 Plane answers to complex questions | 000SA.062 D229 Advances in growth curve models : |
Includes bibliographical references and indexes.
1. Introduction to linear and generalized linear models --
2. Linear models: least squares theory --
3. Normal linear models: statistical inference --
4. Generalized linear models: models fitting and inference --
5. Models for binary data --
6. Multinomial response models --
7. Models for count data --
8. Quasi-likelihood methods --
9. Modeling correlated responses --
10. Bayesian linear and generalized linear modeling --
11. Extensions of generalized linear models--
Appendices--
References--
Indexes.
This book presents an overview of the foundations and the key ideas and results of linear and generalized linear models under one cover. Written by a prolific academic, researcher, and textbook writer, Foundations of Linear and Generalized Linear Models is soon to become the gold standard by which all existing textbooks on the topic will be compared. While the emphasis is clearly and succinctly on theoretical underpinnings, applications in "R" are presented when they help to elucidate the content or promote practical model building. Each chapter contains approximately 15-20 exercises, primarily for readers to practice and extend the theory, but, also to assimilate the ideas by doing some data analysis. The carefully crafted models and examples convey basic concepts and do not get mired down in non-trivial considerations. An author-maintained web site includes, among other numerous pedagogical supplements, analyses that parallel the "R" routines from the book in SAS, SPSS and Stata.
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