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Biometrika Advance Access published online on May 14, 2007
Biometrika, doi:10.1093/biomet/asm028
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Copyright © 2007 Biometrika Trust

Miscellanea

Small-sample degrees of freedom for multi-component significance tests with multiple imputation for missing data

Jerome P. Reiter

Institute of Statistics and Decision Sciences, Duke University, Box 90251, Durham, North Carolina 27708-0251, U.S.A.

jerry{at}stat.duke.edu

Received for publication 1 February 2006. Revision received 1 August 2006.
  Abstract

When performing multi-component significance tests with multiply-imputed datasets, analysts can use a Wald-like test statistic and a reference F-distribution. The currently employed degrees of freedom in the denominator of this F-distribution are derived assuming an infinite sample size. For modest complete-data sample sizes, this degrees of freedom can be unrealistic; for example, it may exceed the complete-data degrees of freedom. This paper presents an alternative denominator degrees of freedom that is always less than or equal to the complete-data denominator degrees of freedom, and equals the currently employed denominator degrees of freedom for infinite sample sizes. Its advantages over the currently employed degrees of freedom are illustrated with a simulation.

Key Words: Missing data • Multiple imputation • Significance test


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