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Uniformly Minimum Variance Estimation in Location Parameter Families

Lennart Bondesson
The Annals of Statistics
Vol. 3, No. 3 (May, 1975), pp. 637-660
Stable URL: http://www.jstor.org/stable/2958433
Page Count: 24
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Uniformly Minimum Variance Estimation in Location Parameter Families
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Abstract

Let x1, ⋯, xn be a sample of size n of an $\operatorname{rv}$ with $\operatorname{df} F(x - \theta)$, where F is known but θ unknown. In this paper we make a Fourier approach to the problem of existence of a statistic g(x1, ⋯, xn) which is a uniformly minimum variance (UMV) estimator of its own mean value. We mention only some of the results. If n = 1 we find an NASC for an estimator g(x1) to be, in a restricted sense, UMV. This condition is given in terms of the zeroes of the ch.f. of F and the support of the Fourier transform of g. If n ≥ 2, it is shown that a statistic of the form g(x̄), where x̄ is the sample mean, cannot be UMV, unless g is periodic or F is a normal $\operatorname{df}$. We prove the non-existence of a UMV-estimator of θ, provided that the tail of F tends to zero rapidly enough. Finally, it is proved that no polynomial P(x1, ⋯, xn) can be a UMV-estimator, unless F is a normal $\operatorname{df}$.

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