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On improving standard estimators via linear empirical Bayes methods

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  • Samaniego, Francisco J.
  • Vestrup, Eric

Abstract

Suppose one wishes to estimate the parameter [theta] in a current experiment when one also has in hand data from k past experiments satisfying empirical Bayes sampling assumptions. It has long been known that, for a variety of models, empirical Bayes estimators tend to outperform, asymptotically, standard estimators based on the current experiment alone. Much less is known about the superiority of empirical Bayes estimators over standard estimators when k is fixed; what is known in that regard is largely the product of Monte Carlo studies. Conditions are given here under which certain linear empirical Bayes estimators are superior to the standard estimator for arbitrary k[greater-or-equal, slanted]1.

Suggested Citation

  • Samaniego, Francisco J. & Vestrup, Eric, 1999. "On improving standard estimators via linear empirical Bayes methods," Statistics & Probability Letters, Elsevier, vol. 44(3), pages 309-318, September.
    • Handle: RePEc:eee:stapro:v:44:y:1999:i:3:p:309-318
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    Citations

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    Cited by:

    1. Allison, David B. & Gadbury, Gary L. & Heo, Moonseong & Fernandez, Jose R. & Lee, Cheol-Koo & Prolla, Tomas A. & Weindruch, Richard, 2002. "A mixture model approach for the analysis of microarray gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 39(1), pages 1-20, March.
    2. Wang, Lichun & Singh, Radhey S., 2014. "Linear Bayes estimator for the two-parameter exponential family under type II censoring," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 633-642.
    3. Wang, Lichun, 2019. "Computing the estimator of a parameter vector via a competing Bayes method," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 165(C), pages 271-279.

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