Statistical Inference: An Integrated Bayesian/Likelihood Approach

Filling a gap in current Bayesian theory, this book presents a unified Bayesian treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. This novel approach provides new solutions to difficult model comparison problems and offers direct Bayesian counterparts of frequentist t-tests and other standard statistical methods for hypothesis testing. The book discusses nonparametric Bayesian survey analysis, gives alternatives to current frequentist nonparametric methods, and includes new goodness-of-fit methods for assessing parametric models. It also covers normal regression, analysis of variance, two-level variance component models, and finite mixtures.