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Reducing data nonconformity in linear models

Andrew L. Rukhin

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Abstract

Procedures to reduce nonconformity in interlaboratory studies by shrinking multivariate data toward a consensus matrix-weighted mean are discussed. Some of them are shown to have a smaller quadratic risk than the ordinary least squares rule. Bayes procedures and shrinkage estimators in random effects models are also considered. The results are illustrated by an example of collaborative studies.

Chapter information

Source
Dominique Fourdrinier, Éric Marchand and Andrew L. Rukhin, eds., Contemporary Developments in Bayesian Analysis and Statistical Decision Theory: A Festschrift for William E. Strawderman (Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2012), 64-77

Dates
First available in Project Euclid: 14 March 2012

Permanent link to this document
https://projecteuclid.org/euclid.imsc/1331731612

Digital Object Identifier
doi:10.1214/11-IMSCOLL805

Zentralblatt MATH identifier
1326.62022

Subjects
Primary: 62C20: Minimax procedures
Secondary: 62F10: Point estimation 62P30: Applications in engineering and industry

Keywords
Birge ratio consensus value jackknife estimator matrix weighted means meta-analysis normal mean shrinkage estimator

Rights
Copyright © 2012, Institute of Mathematical Statistics

Citation

Rukhin, Andrew L. Reducing data nonconformity in linear models. Contemporary Developments in Bayesian Analysis and Statistical Decision Theory: A Festschrift for William E. Strawderman, 64--77, Institute of Mathematical Statistics, Beachwood, Ohio, USA, 2012. doi:10.1214/11-IMSCOLL805. https://projecteuclid.org/euclid.imsc/1331731612


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