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SOME SIMPLE TEST PROCEDURES FOR NORMAL MEAN

VECTOR WITH INCOMPLETE DATA

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K. KRISHNAMOORTHY AND MARUTHY K. PANNALA

*Department of Statistics, University of Southwestern Louisiana, Lafayette,*

LA 70504-1006, U.S.A.
(Received December 25, 1996; revised October 20, 1997)

**Abstract.**
The problem of testing normal mean vector
when the observations are missing from subsets of components
is considered. For a data matrix with a monotone pattern,
three simple exact tests are proposed as alternatives to the
traditional likelihood ratio test. Numerical power comparisons
between the proposed tests and the likelihood ratio test
suggest that one of the proposed tests is indeed comparable to
the likelihood ratio test and the other two tests perform
better than the likelihood ratio test over a part of the
parameter space. The results are extended to a nonmonotone
pattern and illustrated using an example.

*Key words and phrases*:
Fisher's method of
combining independent tests, likelihood ratio test, missing
data, monotone pattern, power, Tippett's test,
union-intersection test.

**Source**
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