Confidence Intervals for a Common Mean with Missing Data with Applications in an AIDS Study
In practical data analysis, nonresponse phenomenon frequently occurs. In this paper, we propose an empirical likelihood based confidence interval for a common mean by combining the imputed data, assuming that data are missing completely at random. Simulation studies show that such confidence intervals perform well, even when the missing proportion is high. Our method is applied to an analysis of a real data set from an AIDS clinic trial study.
MSU Digital Commons Citation
Liang, Hua; Su, Haiyan; and Zou, Guohua, "Confidence Intervals for a Common Mean with Missing Data with Applications in an AIDS Study" (2008). Department of Applied Mathematics and Statistics Faculty Scholarship and Creative Works. 32.