Reducing data nonconformity in linear models

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.