Optimal confidence interval for the largest normal mean under heteroscedasticity

Hubert J. Chen, Miin Jye Wen

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)982-1001
Number of pages20
JournalComputational Statistics and Data Analysis
Volume51
Issue number2
DOIs
Publication statusPublished - 2006 Nov 15

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'Optimal confidence interval for the largest normal mean under heteroscedasticity'. Together they form a unique fingerprint.

Cite this