Statistical Inference: An Integrated Bayesian/Likelihood Approach

Theories of Statistical Inference Example Statistical models The likelihood function Theories Nonmodel-based repeated sampling Conclusion The Integrated Bayes/Likelihood Approach Introduction Probability Prior ignorance The importance of parametrization The simple/simple hypothesis testing problem The simple/composite hypothesis testing problem Posterior likelihood approach Bayes factors The comparison of unrelated models Example-GHQ score and psychiatric diagnosis t-Tests and Normal Variance Tests One-sample t-test Two samples: equal variances The two-sample test Two samples: different variances The normal model variance Variance heterogeneity test Unified Analysis of Finite Populations Sample selection indicators The Bayesian bootstrap Sampling without replacement Regression models More general regression models The multinomial model for multiple populations Complex sample designs A complex example Discussion Regression and Analysis of Variance Multiple regression Nonnested models Binomial and Multinomial Data Single binomial samples Single multinomial samples Two-way tables for correlated proportions Multiple binomial samples Two-way tables for categorical responses-no fixed margins Two-way tables for categorical responses-one fixed margin Multinomial "nonparametric" analysis Goodness of Fit and Model Diagnostics Frequentist model diagnostics Bayesian model diagnostics The posterior predictive distribution Multinomial deviance computation Model comparison through posterior deviances Examples Simulation study Discussion Complex Models The data augmentation algorithm Two-level variance component models Test for a zero variance component Finite mixtures References Author Index Subject Index