For estimating under squared-error loss the mean of a p-variate normal distribution when this mean lies in a ball of radius m centered at the origin and the covariance matrix is equal to the identity matrix, it is shown that the Bayes estimator with respect to a uniformly distributed prior on the bo
A note on admissibility of the maximum likelihood estimator for a bounded normal mean
β Scribed by Manabu Iwasa; Yoshiya Moritani
- Publisher
- Elsevier Science
- Year
- 1997
- Tongue
- English
- Weight
- 279 KB
- Volume
- 32
- Category
- Article
- ISSN
- 0167-7152
No coin nor oath required. For personal study only.
β¦ Synopsis
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.
π SIMILAR VOLUMES
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 es