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Publication Citation: Reducing Data Nonconformity in Linear Models

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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
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