Open Access
VOL. 8 | 2012 Reducing data nonconformity in linear models
Andrew L. Rukhin

Editor(s) Dominique Fourdrinier, Éric Marchand, Andrew L. Rukhin

Inst. Math. Stat. (IMS) Collect., 2012: 64-77 (2012) DOI: 10.1214/11-IMSCOLL805


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.


Published: 1 January 2012
First available in Project Euclid: 14 March 2012

zbMATH: 1326.62022

Digital Object Identifier: 10.1214/11-IMSCOLL805

Primary: 62C20
Secondary: 62F10 , 62P30

Keywords: Birge ratio , consensus value , jackknife estimator , matrix weighted means , Meta-analysis , normal mean , shrinkage estimator

Rights: Copyright © 2012, Institute of Mathematical Statistics

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