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Can I read Optimal Confidence Interval for the Largest Normal Mean Under Heteroscedasticity on EtoBox?
Optimal Confidence Interval for the Largest Normal Mean Under Heteroscedasticity by Hubert J. Chen; Miin-Jye Wen is a Mathematics article available to read on EtoBox.
What is Optimal Confidence Interval for the Largest Normal Mean Under Heteroscedasticity about?
A two-stage sampling procedure for obtaining an optimal confidence interval for the largest or smallest mean of k independent normal populations is proposed, where the population variances are unknown and possibly unequal. The optimal confidence interval is obtained by maximizing the coverage probability with a fixed width at a least favorable configuration of means. Then, the sample sizes can be determined by this procedure. It has been shown that the optimal interval is globally optimal over all possible choices of symmetric and asymmetric intervals. In situations where the two-stage sampling procedure cannot be completely carried through, a one-stage sampling procedure can be implemented, and their relationship is discussed. A numerical example to demonstrate the use of these sampling procedures is given.
Who reads Optimal Confidence Interval for the Largest Normal Mean Under Heteroscedasticity?
It is typically read by researchers, students, and practitioners in Mathematics.
- Author
- Hubert J. Chen; Miin-Jye Wen
- Publisher
- Elsevier Science; Elsevier ; Elsevier BV (ISSN 0167-9473)
- Published
- 2006
- Language
- EN
- Field
- Mathematics (Physical Sciences)