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A note on admissibility of the maximum likelihood estimator for a bounded normal mean

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  • Iwasa, Manabu
  • Moritani, Yoshiya

Abstract

In estimating a bounded normal mean, it is known that the maximum likelihood estimator is inadmissible for squared error loss function. In this paper, we discuss the admissibility for other loss functions. We prove that the maximum likelihood estimator is admissible under absolute error loss.

Suggested Citation

  • Iwasa, Manabu & Moritani, Yoshiya, 1997. "A note on admissibility of the maximum likelihood estimator for a bounded normal mean," Statistics & Probability Letters, Elsevier, vol. 32(1), pages 99-105, February.
    • Handle: RePEc:eee:stapro:v:32:y:1997:i:1:p:99-105
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    References listed on IDEAS

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    1. Wolfgang Bischoff & Werner Fieger, 1993. "On least favourable two point priors and minimax estimators under absolute error loss," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 40(1), pages 283-298, December.
    2. J. Eichenauer-Herrmann & K. Ickstadt, 1992. "Minimax estimators for a bounded location parameter," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 39(1), pages 227-237, December.
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    Cited by:

    1. Kucerovsky Dan & Marchand Eric & Najafabadi Amir T. Payandeh & Strawderman William E., 2009. "On the Bayesianity of maximum likelihood estimators of restricted location parameters under absolute value error loss," Statistics & Risk Modeling, De Gruyter, vol. 27(02), pages 145-168, December.
    2. Marchand Éric & MacGibbon Brenda, 2000. "Minimax Estimation Of A Constrained Binomial Proportion," Statistics & Risk Modeling, De Gruyter, vol. 18(2), pages 129-168, February.

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    1. Wolfgang Bischoff & Werner Fieger, 1993. "On least favourable two point priors and minimax estimators under absolute error loss," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 40(1), pages 283-298, December.

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