The Annals of Statistics

Combining Independent Noncentral Chi Squared or $F$ Tests

John I. Marden

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The problem of combining several independent Chi squared or $F$ tests is considered. The data consist of $n$ independent Chi squared or $F$ variables on which tests of the null hypothesis that all noncentrality parameters are zero are based. In each case, necessary conditions and sufficient conditions for a test to be admissible are given in terms of the monotonicity and convexity of the acceptance region. The admissibility or inadmissibility of several tests based upon the observed significance levels of the individual test statistics is determined. In the Chi squared case, Fisher's and Tippett's procedures are admissible, the inverse normal and inverse logistic procedures are inadmissible, and the test based upon the sum of the significance levels is inadmissible when the level is less than a half. The results are similar, but not identical, in the $F$ case. Several generalized Bayes tests are derived for each problem.

Article information

Ann. Statist., Volume 10, Number 1 (1982), 266-277.

First available in Project Euclid: 12 April 2007

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier


Primary: 62C07: Complete class results
Secondary: 62C15: Admissibility 62H15: Hypothesis testing 62C10: Bayesian problems; characterization of Bayes procedures

Hypothesis tests generalized Bayes tests Chi squared variables $F$ variables admissibility complete class significance levels combination procedures


Marden, John I. Combining Independent Noncentral Chi Squared or $F$ Tests. Ann. Statist. 10 (1982), no. 1, 266--277. doi:10.1214/aos/1176345709.

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