Bayesian estimation and experimental design in linear regression models

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Last edited by ImportBot
May 27, 2020 | History

Bayesian estimation and experimental design in linear regression models

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Presents a clear treatment of the design and analysis of linear regression experiments in the presence of prior knowledge about the model parameters. Develops a unified approach to estimation and design; provides a Bayesian alternative to the least squares estimator; and indicates methods for the construction of optimal designs for the Bayes estimator. Material is also applicable to some well-known estimators using prior knowledge that is not available in the form of a prior distribution for the model parameters; such as mixed linear, minimax linear and ridge-type estimators.

Publish Date
Publisher
Wiley
Language
English
Pages
296

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Previews available in: English

Book Details


Edition Notes

Includes bibliographical references ([275]-296) and index.

Published in
Chichester, New York
Series
Wiley series in probability and mathematical statistics

Classifications

Dewey Decimal Class
519.5
Library of Congress
QA279 .P55 1991, QA279.P55 1991

The Physical Object

Pagination
x, 296 p. ;
Number of pages
296

ID Numbers

Open Library
OL2398016M
Internet Archive
bayesianestimati0000pilz
ISBN 10
047191732X
LCCN
87027915
Library Thing
2799016
Goodreads
7046685

Links outside Open Library

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History

Download catalog record: RDF / JSON
May 27, 2020 Edited by ImportBot import existing book
April 21, 2019 Edited by Kaustubh Chakraborty Updated content link
April 21, 2019 Edited by Kaustubh Chakraborty Added description
April 21, 2019 Edited by Kaustubh Chakraborty Added new cover
December 10, 2009 Created by WorkBot add works page