Confidence intervals for a common mean with missing data with applications in an AIDS study

Authors:
Hua Liang;Haiyan Su;Guohua Zou
Affiliations:
Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY 14642, USA;Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY 14642, USA;Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
Venue:
Computational Statistics & Data Analysis
Year:
2008

Citing 2
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Statistical analysis with missing data

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Abstract

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.