Multivariate Statistical Modelling Based on Generalized Linear Models

The book is aimed at applied statisticians, graduate students of statistics, and students and researchers with a strong interest in statistics and data analysis from areas like econometrics, biometrics, and and social sciences.

New edition conatins extended coverage of Bayesian concepts which are of growing importance Analyzes real data drawn from diverse fields, incl. biology, economics, and the social sciences Modern methods such as Monte Carlo Markov Chains, survival analysis, and the EM algortihm are discussed

Inhalt

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 and Hidden Markov Models.- 9. Survival Models.- 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.- B. Software for Fitting Generalized Linear Models and Extensions.- Author Index.