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Activity Number: 74
Type: Contributed
Date/Time: Sunday, August 6, 2006 : 4:00 PM to 5:50 PM
Sponsor: Biometrics Section
Abstract - #306560
Title: Multivariate One-Sided Hypotheses Testing with Complete or Incomplete Data
Author(s): Tao Wang*+ and Lang Wu
Companies: The University of British Columbia and The University of British Columbia
Address: Box 592, Vancouver, BC, v6t 2g9, Canada
Keywords: multivariate one-sided hypotheses ; bootstrap ; missing data ; likelihood ratio test ; observed-data likelihood ; EM
Abstract:

Multivariate one-sided hypotheses testing problems arise frequently in practice. Up to now some testing methods have been proposed. However, most of them require normal or large sample assumptions, which may not be reasonable. Also, the commonly-used likelihood-based tests are conservative because the null distributions often depend on nuisance parameters. Attempting to solve these problems, a bootstrap test method will be proposed. On the other hand, all of the existing tests only apply to complete data, but in practice there are often missing data. So a likelihood ratio test will be proposed for incomplete data.

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