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Inadmissibility of the Maximum Likelihood Estimator in the Presence of Prior Information

  • B. K. Kale (a1)

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Lehmann [1] in his lecture notes on estimation shows that for estimating the unknown mean of a normal distribution, N(θ, 1), the usual estimator is neither minimax nor admissible if it is known that θ belongs to a finite closed interval [a, b] and the loss function is squared error. It is shown that , the maximum likelihood estimator (MLE) of θ, has uniformly smaller mean squared error (MSE) than that of . It is natural to ask the question whether the MLE of θ in N(θ, 1) is admissible or not if it is known that θ ∊ [a, b]. The answer turns out to be negative and the purpose of this note is to present this result in a slightly generalized form.

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References

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1. Lehmann, E. L., Notes on theory of estimation, Associated Students' Store. Univ. of California, 1949.
2. Lehmann, E. L., Testing statistical hypotheses, Wiley, New York, 1959.
3. Wald, A., Statistical decision functions, Wiley, New York, 1950.
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Inadmissibility of the Maximum Likelihood Estimator in the Presence of Prior Information

  • B. K. Kale (a1)

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