Bayesian statistical modelling - Congdon, P. (Chichester, England ; Hoboken, NJ : John Wiley & Sons, 2006)
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 eqations.
Includes bibliographical references and index.
Keywords: Bayesian statistical decision theory