The author's research has been directed towards inference involving observables rather than parameters. In this book, he brings together his views on predictive or observable inference and its advantages over parametric inference. While the book discusses a variety of approaches to prediction including those based on parametric, nonparametric, and nonstochastic statistical models, it is devoted mainly to predictive applications of the Bayesian approach. It not only substitutes predictive analyses for parametric analyses, but it also presents predictive analyses that have no real parametric analogues. It demonstrates that predictive inference can be a critical component of even strict parametric inference when dealing with interim analyses. This approach to predictive inference will be of interest to statisticians, psychologists, econometricians, and sociologists.
Table of Contents
1. Introduction 2. Non-Bayesian Predictive Approaches 3. Bayesian prediction 4. Selecting a Statistical Model and Predicting 5. Problems of comparison and allocation 6. Perturbation Analysis 7. Process control and optimization 8. Screening tests for detecting a characteristic 9. Multivariate normal prediction 10. Interim analysis and sampling curtailment