Inadmissibility of the Maximum Likelihood Estimator in the Presence of Prior Information
Canadian mathematical bulletin, Tome 13 (1970) no. 3, pp. 391-393

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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.
Kale, B. K. Inadmissibility of the Maximum Likelihood Estimator in the Presence of Prior Information. Canadian mathematical bulletin, Tome 13 (1970) no. 3, pp. 391-393. doi: 10.4153/CMB-1970-076-x
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     title = {Inadmissibility of the {Maximum} {Likelihood} {Estimator} in the {Presence} of {Prior} {Information}},
     journal = {Canadian mathematical bulletin},
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     year = {1970},
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     doi = {10.4153/CMB-1970-076-x},
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