Bayesian Statistical Modelling

Peter Congdon - Collection Wiley Series in Probability and Statistics

596 pages, parution le 24/11/2006 (2^eme édition)

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Résumé

Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as WINBUGS and R have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics.

Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets.

The second edition:

Provides an integrated presentation of theory, examples, applications and computer algorithms.
Discusses the role of Markov Chain Monte Carlo methods in computing and estimation.
Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences.
Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles.
Provides exercises designed to help reinforce the reader's knowledge and a supplementary website containing data sets and relevant programs.

Bayesian Statistical Modelling is ideal for researchers in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students.

Sommaire

Introduction: The Bayesian Method, its Benefits and Implementation
Bayesian Model Choice, Comparison and Checking
The Major Densities and their Application
Normal Linear Regression, General Linear Models and Log-Linear Models
Hierarchical Priors for Pooling Strength and Overdispersed Regression Modelling
Discrete Mixture Priors
Multinomial and Ordinal Regression Models
Time Series Models
Modelling Spatial Dependencies
Nonlinear and Nonparametric Regression
Multilevel and Panel Data Models
Latent Variable and Structural Equation Models for Multivariate Data
Survival and Event History Analysis
Missing Data Models
Measurement Error, Seemingly Unrelated Regressions, and Simultaneous Equations
Appendix 1: A Brief Guide to Using WINBUGS
Index

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Wiley
Auteur(s)	Peter Congdon
Collection	Wiley Series in Probability and Statistics
Parution	24/11/2006
Édition	2^eme édition
Nb. de pages	596
Format	17 x 25
Couverture	Relié
Poids	1277g
Intérieur	Noir et Blanc
EAN13	9780470018750
ISBN13	978-0-470-01875-0