Take a sneak peek at the new NIST.gov and let us know what you think!
(Please note: some content may not be complete on the beta site.).

View the beta site
NIST logo

Publication Citation: Reducing Data Nonconformity in Linear Models

NIST Authors in Bold

Author(s): Andrew L. Rukhin;
Title: Reducing Data Nonconformity in Linear Models
Published: January 03, 2011
Abstract: Stein phenomenon Summary Several procedures designed to reduce nonconformity in interlaboratory studies by shrinking data toward a consensus matrix weighted mean are suggested. Some of them are shown to have a smaller quadratic risk than the vector sample mean. Shrinkage toward a weighted means statistics appearing in random effects model. The results are illustrated by an example of collaborative studies.
Citation: IMS Lecture Notes-Mongraph Series
Keywords: Birge ratio, DerSimonian-Laird estimator, heteroscedasticity, key comparisons, meta-analysis, normal mean, reference value, shrinkage estimators
Research Areas: Statistics
PDF version: PDF Document Click here to retrieve PDF version of paper (219KB)