Bayesian estimation and experimental design in linear regression models

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Last edited by MARC Bot
October 31, 2020 | History

Bayesian estimation and experimental design in linear regression models

1. Aufl.
  • 1 Currently reading

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
Teubner
Language
English
Pages
216

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Previews available in: English

Book Details


Edition Notes

Bibliography: p. 206-215.

Published in
Leipzig
Series
Teubner-Texte zur Mathematik,, Bd. 55

Classifications

Dewey Decimal Class
519.5/35
Library of Congress
QA279 .P55 1983

The Physical Object

Pagination
216 p. ;
Number of pages
216

Edition Identifiers

Open Library
OL2884672M
LCCN
84108480

Work Identifiers

Work ID
OL4979119W

Links outside Open Library

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History

Download catalog record: RDF / JSON / OPDS | Wikipedia citation
October 31, 2020 Edited by MARC Bot import existing book
April 21, 2019 Edited by Kaustubh Chakraborty Added new cover
December 12, 2009 Edited by WorkBot link works
April 1, 2008 Created by an anonymous user Imported from Scriblio MARC record