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 May 28, 2023)
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Reducing Data Nonconformity in Linear Models
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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
Pub Type
Others
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Keywords
Birge ratio, DerSimonian-Laird estimator, heteroscedasticity, key comparisons, meta-analysis, normal mean, reference value, shrinkage estimators
Citation
Created January 3, 2011, Updated February 19, 2017