|aBayesian statistical modelling /|cby Peter Congdon.
|aChichester, England :|bJohn Wiley & Sons, |c c2006.
|axi, 573 p. :|bill. ;|c25 cm.
|aWiley series in probability and statistics
|aIncludes bibliographical references and index.
|aIntroduction : 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.