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.
A note on second-order admissibility of the Graybill-Deal estimator of a common mean of several normal populations
โ Scribed by Nabendu Pal; Wooi K. Lim
- Publisher
- Elsevier Science
- Year
- 1997
- Tongue
- English
- Weight
- 295 KB
- Volume
- 63
- Category
- Article
- ISSN
- 0378-3758
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โฆ Synopsis
Consider the problem of estimating the common mean of several normal populations with unknown and possibly unequal variances under the squared error loss function. The well-known unbiased estimator of the common mean is the Graybill-Deal estimator. The general admissibility (or otherwise) of this estimator is still an open problem even though Sinha and Mouqadem (Comm. Statist. Theory Methods 11 (1982) 1603-1614) proved some restricted admissibility results. In this paper, we follow the asymptotic decision-theoretic approach of "second-order admissibility' as adopted by Ghosh and Sinha (Ann. Statist. 9 (1981) 1334-1338), and prove that the GraybiU-Deal estimator is second-order admissible.
๐ SIMILAR VOLUMES
For estimating the common mean of k (~>2) normal populations with order restricted unknown variances, using the theory of isotonic regression, we propose a simple estimator which is better than the usual Graybill-Deal estimator in terms of stochastic dominance and the Pitman measure of closeness. C~