Multivariate Statistical Modelling Based on Generalized Linear Models
Classical statistical models for regression, time series and longitudinal data provide well-established tools for approximately normally distributed vari ables. Enhanced by the availability of software packages these models dom inated the field of applications for a long time. With the introductio...
| Main Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
New York, NY
Springer New York
1994, 1994
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| Edition: | 1st ed. 1994 |
| Series: | Springer Series in Statistics
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| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- 1 Introduction
- 2 Modelling and analysis of cross-sectional data: a review of univariate generalized linear models
- 3 Models for multicategorical responses: multivariate extensions of generalized linear models
- 4 Selecting and checking models
- 5 Semi— and nonparametric approaches to regression analysis
- 6 Fixed parameter models for time series and longitudinal data
- 7 Random effects models
- 8 State space models
- 9 Survival models
- Appendix A
- A.1 Exponential families and generalized linear models
- A.2 Basic ideas for asymptotics
- A.3 EM—algorithm
- A.4 Numerical integration
- A.5 Monte Carlo methods
- Appendix B Software for fitting generalized linear models
- References
- Author Index