Generalized Linear Models

A Bayesian Perspective
1. Auflage 2000
ISBN: 978-0-8247-9034-9
Verlag: Taylor & Francis Inc

Buch, Englisch, 442 Seiten, Format (B × H): 178 mm x 260 mm, Gewicht: 830 g

Reihe: Chapman & Hall/CRC Biostatistics Series

This volume describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective and how to use modern computational methods to summarize inferences using simulation. Introducing dynamic modeling for GLMs and containing over 1000 references and equations, Generalized Linear Models considers parametric and semiparametric approaches to overdispersed GLMs, presents methods of analyzing correlated binary data using latent variables. It also proposes a semiparametric method to model link functions for binary response data, and identifies areas of important future research and new applications of GLMs.

Zielgruppe

Statisticians, biostatisticians, and applied statisticians in the pharmaceutical industry and medical centers, and graduate-level students in these disciplines.

Autoren/Hrsg.

Roberts, C. D.

Dey, Dipak K.

Ghosh, Sujit K.

Mallick, Bani K.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik

Inhaltsverzeichnis

Part 1 Extending the GLMs. Part 2 Categorical and longitudinal data. Part 3 Semiparametric approaches. Part 4 Model diagnositics and value selection in GLMs. Part 5 Challenging problems in GLMs

Über Autor(innen)

Dipak K. Dey, Sujit K. Ghosh, Bani K. Mallick