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Reducing Data Nonconformity in Linear Models

Published

Author(s)

Andrew L. Rukhin

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

Citation

Rukhin, A. (2011), Reducing Data Nonconformity in Linear Models, IMS Lecture Notes-Mongraph Series, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=905278 (Accessed July 18, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created January 3, 2011, Updated February 19, 2017