Seymour Geisser

Predictive Inference

Herausgegeben:Cox, D. R.; Reid, N.; Isham, Valerie; Tibshirani, R.J.; Louis, Thomas A.; Tong, Howell; Keiding, Niels

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Chapman & Hall/CRC Monographs on Statistics and Applied Probability
• Verlag: Springer / Springer Netherlands / Springer US
• Artikelnr. des Verlages: C3471
• Softcover reprint of the original 1st ed. 1993
• Seitenzahl: 276
• Erscheinungstermin: 1. Juni 1993
• Englisch
• Abmessung: 229mm x 140mm x 20mm
• Gewicht: 359g
• ISBN-13: 9780412034718
• ISBN-10: 0412034719
• Artikelnr.: 22077297

Autorenporträt

Geisser\, Seymour

Inhaltsangabe

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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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

Rezensionen

"...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