Empirical Bayes Estimation for Combinations of Multivariate Bioassays
Authors: Chen D.G.1; Carter E.M.2; Hubert J.J.2; Kim P.T.2
Source: Biometrics, Volume 55, Number 4, December 1999 , pp. 1038-1043(6)
Publisher: Blackwell Publishing
- Free Content
- New Content
- Subscribed Content
- Free Trial Content
Abstract:
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.Keywords: E-M algorithm; Jeffreys' rules; Posterior distribution; Prior information; Relative potency
Document Type: Research article
DOI: 10.1111/j.0006-341X.1999.01038.x
Affiliations: 1: Pacific Biological Station, Department of Fisheries and Oceans, Government of Canada, 3190 Hammond Bay Road, Nanaimo, British Columbia V9R 5K6, Canada 2: Department of Mathematics and Statistics, University of Guelph, Guelph, Ontario NIG 2W1, Canada
- Free Content
- New Content
- Subscribed Content
- Free Trial Content
Click here for Page Help