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

Hua Liang; Haiyan Su; Guohua Zou

doi:10.1016/j.csda.2008.09.021

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

Hua Liang, Haiyan Su, Guohua Zou

Applied Mathematics and Statistics

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

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.

Original language	English
Pages (from-to)	546-553
Number of pages	8
Journal	Computational Statistics and Data Analysis
Volume	53
Issue number	2
DOIs	https://doi.org/10.1016/j.csda.2008.09.021
State	Published - 15 Dec 2008

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10.1016/j.csda.2008.09.021

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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.",

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AU - Su, Haiyan

AU - Zou, Guohua

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N2 - 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.

AB - 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.

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M3 - Article

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EP - 553

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

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ER -

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

Abstract

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