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A class of general pretest estimators for the univariate normal mean

Author

Listed:
  • Jia-Han Shih
  • Yoshihiko Konno
  • Yuan-Tsung Chang
  • Takeshi Emura

Abstract

In this paper, we propose a class of general pretest estimators for the univariate normal mean. The main mathematical idea of the proposed class is the adaptation of randomized tests, where the randomization probability is related to a shrinkage parameter. Consequently, the proposed class includes many existing estimators, such as the pretest, shrinkage, Bayes, and empirical Bayes estimators as special cases. Furthermore, the proposed class can be easily tuned for users by adjusting significance levels and probability function. We derive theoretical properties of the proposed class, such as the expressions for the distribution function, bias, and MSE. Our expressions for the bias and MSE turn out to be simpler than those previously derived for some existing formulas for special cases. We also conduct simulation studies to examine our theoretical results and demonstrate the application of the proposed class through a real dataset.

Suggested Citation

  • Jia-Han Shih & Yoshihiko Konno & Yuan-Tsung Chang & Takeshi Emura, 2023. "A class of general pretest estimators for the univariate normal mean," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 52(8), pages 2538-2561, April.
    • Handle: RePEc:taf:lstaxx:v:52:y:2023:i:8:p:2538-2561
    DOI: 10.1080/03610926.2021.1955384
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