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Information Journal Paper

Title

ESTIMATING A BOUNDED NORMAL MEAN UNDER THE LINEX LOSS FUNCTION

Pages

  157-164

Abstract

 Let X be a random variable from a NORMAL DISTRIBUTION with unknown mean q and known variance  s2. In many practical situations, q is known in advance to lie in an interval, say [- m, m], for some m>0. As the usual estimator of q, i.e., X under the LINEX LOSS FUNCTION is inadmissible, finding some competitors for X becomes worthwhile. The only study in the literature considered the problem of minimax estimation of q In this paper, by constructing a dominating class of estimators, we show that the MAXIMUM LIKELIHOOD ESTIMATOR is inadmissible. Then, as a competitor, the BAYES ESTIMATOR associated with a uniform prior on the interval [- m, m] is proposed. Finally, considering risk performance as a comparison criterion, the estimators are compared and depending on the values taken by q in the interval [- m, m], the appropriate estimator is suggested.

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    • Cite

    APA: Copy

    KARIMNEZHAD, A.. (2013). ESTIMATING A BOUNDED NORMAL MEAN UNDER THE LINEX LOSS FUNCTION. JOURNAL OF SCIENCES ISLAMIC REPUBLIC OF IRAN, 24(2), 157-164. SID. https://sid.ir/paper/592300/en

    Vancouver: Copy

    KARIMNEZHAD A.. ESTIMATING A BOUNDED NORMAL MEAN UNDER THE LINEX LOSS FUNCTION. JOURNAL OF SCIENCES ISLAMIC REPUBLIC OF IRAN[Internet]. 2013;24(2):157-164. Available from: https://sid.ir/paper/592300/en

    IEEE: Copy

    A. KARIMNEZHAD, “ESTIMATING A BOUNDED NORMAL MEAN UNDER THE LINEX LOSS FUNCTION,” JOURNAL OF SCIENCES ISLAMIC REPUBLIC OF IRAN, vol. 24, no. 2, pp. 157–164, 2013, [Online]. Available: https://sid.ir/paper/592300/en

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