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Empirical Bayes Estimation for Combinations of Multivariate Bioassays
Author(s) -
Chen D. G.,
Carter E. M.,
Hubert J. J.,
Kim P. T.
Publication year - 1999
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.1999.01038.x
Subject(s) - multivariate statistics , estimator , bioassay , statistics , bayes' theorem , shrinkage estimator , mathematics , multivariate analysis of variance , bayes estimator , econometrics , variance (accounting) , efficient estimator , minimum variance unbiased estimator , biology , bayesian probability , ecology , accounting , business
Summary. This article presents a new empirical Bayes estimator (EBE) and a shrinkage estimator for determining the relative potency from several multivariate bioassays by incorporating prior information on the model parameters based on Jeffreys' rules. The EBE can account for any extra variability among the bioassays, and if this extra variability is 0, then the EBE reduces to the maximum likelihood estimator for combinations of multivariate bioassays. The shrinkage estimator turns out to be a compromise of the prior information and the estimator from each multivariate bioassay, with the weights depending on the prior variance.