Predictive Inference

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CRC Press, Jun 1, 1993 - Mathematics - 240 pages
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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.
  

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Contents

Preface
1
Bayesian prediction
46
Problems of comparison and allocation
118
Perturbation analysis
129
Screening tests for detecting a characteristic
169
Mullivariate normal prediction
191
Interim analysis and sampling curtailment
222
Bibliography
252
Author Index
259
Copyright

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About the author (1993)

SEYMOUR GEISSER, PHD, was Professor of Statistics and Director of the School of Statistics, University of Minnesota, for more than thirty years. In addition to publishing more than 150 scholarly papers and reviews, Dr. Geisser was the Editor of four important volumes in statistical inference and applications, and the author of the unique and critically acclaimed book Predictive Inference.