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On Tests for Correlated Proportions in the Presence of Incomplete Data

Published online by Cambridge University Press:  01 January 2025

D. V. Gokhale  and
B. W. Sirtonik
D. V. Gokhale*
Affiliation:
Department of Statistics, University of California, Riverside
B. W. Sirtonik
Affiliation:
Department of Marketing and Management Science, California State College, San Bernardino
*
Requests for reprints should be sent to D. V. Gokhale, Department of Statistics, University of California, Riverside, CA. 92621.
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Abstract

This paper proposes test statistics based on the likelihood ratio principle for testing equality of proportions in correlated data with additional incomplete samples. Powers of these tests are compared through Monte Carlo simulation with those of tests proposed recently by Ekbohm (based on an unbiased estimator) and Campbell (based on a Pearson-Chi-squared type statistic). Even though tests based on the maximum likelihood principle are theoretically expected to be superior to others, at least asymptotically, results from our simulations show that the gain in power could only be slight.

Keywords

Monte Carlo SimulationLikelihood RatioPublic PolicyStatistical TheoryIncomplete Data

Information

Type
Notes And Comments
Information
Psychometrika , Volume 49 , Issue 1 , March 1984 , pp. 147 - 152
Copyright
Copyright © 1984 The Psychometric Society

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References

Bishop, Y. M. M., Fienberg, S., & Holland, P. W. (1975). Discrete multivariate analysis, Cambridge, MA: The MIT Press.Google Scholar
Campbell, G. (1980). Testing equality of proportions with incomplete correlated data, West Lafayette, IN: Purdue University, Department of Statistics.Google Scholar
Campbell, G. (1983). Testing equality of proportions with incomplete correlated data. (Unpublished manuscript).Google Scholar
Choi, S. C., & Stablein, D. M. (1982). Practical tests for comparing two proportions with incomplete data. Applied Statistics, 31, 256–262.CrossRefGoogle Scholar
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39, 1–22.CrossRefGoogle Scholar
Ekbohm, G. (1982). On testing the equality of proportions in the paired case with incomplete data. Psychometrika, 47, 115–118.CrossRefGoogle Scholar
Gokhale, D. V., & Kullback, S. (1978). The information in contingency tables, New York: Marcel Dekker, Inc..Google Scholar
Hoeffding, W. (1965). Asymptotically optimal tests for multinomial distributions. Annals of Mathematical Statistics, 36, 369–401.CrossRefGoogle Scholar