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A two-stage procedure for estimating a linear function of K multinormal mean vectors when covariance matrices are unknown |
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| Authors: | Makoto Aoshima Yoshikazu Takada Muni S. Srivastava |
| Affiliation: | a Institute of Mathematics, University of Tsukuba, Ibaraki 305-8571, Japan b Department of Computer Science, Kumamoto University, Kumamoto 860-8555, Japan c Department of Statistics, University of Toronto, Toronto, Ontario, Canada M5S 3G3 |
| Abstract: | We consider the problem of constructing a fixed-size confidence region for a linear function of mean vectors of k multinormal populations, where all covariance matrices are completely unknown. A two-stage procedure is proposed to construct such a confidence region. It is shown that the proposed two-stage procedure is consistent and its asymptotic property for the expected sample size is also given. A Monte Carlo simulation study is given for an illustration. |
| Keywords: | Asymptotic efficiency Consistency Fixed-size confidence region Multinormal populations Simulation Two-stage procedure |
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