We consider the problem of estimating the sum of squared error loss \(L=|\beta-\hat{\beta}|^{2}\) of the least-squares esitmator \(\hat{\beta}\) for \(\beta\), the regression coefficient. The standard estimator \(\ell_{0}\) is the expected value of \(L\). Here the error variance is assumed to be kno
Estimated confidence under ancillary statistic everywhere-valid constraint
✍ Scribed by Hsiuying Wang
- Book ID
- 104340467
- Publisher
- Elsevier Science
- Year
- 1998
- Tongue
- English
- Weight
- 473 KB
- Volume
- 67
- Category
- Article
- ISSN
- 0378-3758
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✦ Synopsis
Consider the problem of estimating the coverage function of an usual confidence interval for a randomly chosen linear combination of the elements of the mean vector of a p-dimensional normal distribution. The usual constant coverage probability estimator is shown to be admissible under the ancillary statistic everywhere-valid constraint. Note that this estimator is not admissible under the usual sense if p 7> 5. Since the criterion of admissibility under the ancillary statistic everywhere-valid constraint is a reasonable one, that the constant coverage probability estimator has been commonly accepted is justified. (~) 1998 Elsevier Science B.V. All rights reserved.
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