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.
General
Imprint: |
Chapman & Hall/CRC
|
Country of origin: |
United States |
Series: |
Chapman & Hall/CRC Monographs on Statistics and Applied Probability |
Release date: |
June 1993 |
First published: |
1993 |
Authors: |
Seymour Geisser
|
Dimensions: |
280 x 210 x 20mm (L x W x T) |
Format: |
Hardcover
|
Pages: |
282 |
Edition: |
Softcover Repri |
ISBN-13: |
978-0-412-03471-8 |
Categories: |
Books >
Science & Mathematics >
Mathematics >
Probability & statistics
Promotions
|
LSN: |
0-412-03471-9 |
Barcode: |
9780412034718 |
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