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

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About the Author

Geisser, Seymour

Reviews

"...this monograph is a very welcome attempt to shift back the main emphasis of statistics from parametric estimation and testing to prediction which, as noted by the author, was originally the earliest and most prealent form of statistical inference...I am sure all statisticians and students of statistics with an open mind will enjoy reading it and, hopefully will appreciate the beauty and usefulness of a coherent predictive view of their subject."
-Mathematical Reviews

"Predictive Inference: An Introduction is rich both in the coverage of topics and in applications...The monograph is addressed to statisticians and research workers who are intrested in the predictive approach. Its major contribution is likely to be as a resource for persons interested in trying predictive inference in some application."
-Journal of the ASA