Annals of the Institute of Statistical Mathematics

, Volume 63, Issue 6, pp 1141–1163 | Cite as

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



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.


Parametric hypothesis testing Most powerful test Likelihood ratio Missing data Maximum likelihood 


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Copyright information

© The Institute of Statistical Mathematics, Tokyo 2010

Authors and Affiliations

  1. 1.Department of BiostatisticsThe State University of New York at BuffaloBuffaloUSA
  2. 2.Division of BiostatisticsMedical College of WisconsinMilwaukeeUSA

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