Annals of the Institute of Statistical Mathematics

, Volume 63, Issue 6, pp 1141–1163

An optimal approach for hypothesis testing in the presence of incomplete data

Authors

    • Department of BiostatisticsThe State University of New York at Buffalo
  • Sergey Tarima
    • Division of BiostatisticsMedical College of Wisconsin
Article

DOI: 10.1007/s10463-010-0270-0

Cite this article as:
Vexler, A. & Tarima, S. Ann Inst Stat Math (2011) 63: 1141. doi:10.1007/s10463-010-0270-0

Abstract

The adverse effect of small sample sizes, excessive nonresponse rate, and high dimensionality on likelihood ratio test statistic can be reduced by integrating with respect to a prior distribution. If information regarding the prior is too general (for example, only a parametric family can be specified), this distribution can be chosen from a principle of the most powerful testing. We propose the integrated most powerful test in the presence of missing data. This test can be used as a viable alternative to the maximum likelihood.

Keywords

Parametric hypothesis testingMost powerful testLikelihood ratioMissing dataMaximum likelihood

Copyright information

© The Institute of Statistical Mathematics, Tokyo 2010