Testing equality of correlated proportions with incomplete data on both responses
Two test statistics are proposed for testing the equality of two correlated proportions when some observations are missing on both responses. The performance of these tests in terms of size and power is compared with other tests by means of Monte Carlo simulations. The proposed tests are easily computed and compare favorably with other tests.
Key wordscombination of tests equality of correlated proportions incomplete data asymptotically most powerful test Monte Carlo study antithetic variates power comparison
Unable to display preview. Download preview PDF.
- Bhoj, D. S. (1978). Testing equality of means of correlated variates with missing observations on both responses.Biometrika, 65, 225–228.Google Scholar
- Bhoj, D. S. (1979). Testing equality of variances of correlated variates with incomplete data on both responses.Biometrika, 66, 681–683.Google Scholar
- Choi, S. C., & Stablein, D. M. (1982). Practical tests for comparing two proportions with incomplete data.Applied Statistics, 31, 256–262.Google Scholar
- Hammersley, J. M., & Handscomb, D. C. (1964). Monte Carlo methods. London: Methuen.Google Scholar
- McNemar, Q. (1947). Note on the sampling error of the difference between correlated proportions or percentages.Psychometrika, 12, 153–157.Google Scholar
- Snijders, T. A. B. (1979). Asymptotic optimality theory for testing problems with restricted alternatives.Mathematical Centre Tracks (No. 1B), Amsterdam: Stichting Mathematisch Centrum.Google Scholar
- Snijders, T. A. B. (1984). Antithetic variates for Monte Carlo estimation of probabilities.Statistica Neerlandica, 38, 55–73.Google Scholar
- Whitt, W. (1976). Bivariate distributions with given marginals.Annals of Statistics, 4, 1280–1289.Google Scholar