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Social and Behavioral Sciences Commons™

Open Access. Powered by Scholars. Published by Universities.®

2009

Singapore Management University

Empirical likelihood

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Full-Text Articles in Social and Behavioral Sciences

Empirical Likelihood In Missing Data Problems, Jing Qin, Biao Zhang, Denis H. Y. Leung Dec 2009

Empirical Likelihood In Missing Data Problems, Jing Qin, Biao Zhang, Denis H. Y. Leung

Research Collection School Of Economics

Missing data is a ubiquitous problem in medical and social sciences. It is well known that inferences based only on the complete data may not only lose efficiency, but may also lead to biased results if the data is not missing completely at random (MCAR). The inverse-probability weighting method proposed by Horvitz and Thompson (1952) is a popular alternative when the data is not MCAR. The Horvitz–Thompson method, however, is sensitive to the inverse weights and may suffer from loss of efficiency. In this paper, we propose a unified empirical likelihood approach to missing data problems and explore the use …

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Efficient Parameter Estimation In Longitudinal Data Analysis Using A Hybrid Gee Method, Denis H. Y. Leung, You Gan Wang, Min Zhu Jul 2009

Efficient Parameter Estimation In Longitudinal Data Analysis Using A Hybrid Gee Method, Denis H. Y. Leung, You Gan Wang, Min Zhu

Research Collection School Of Economics

The method of generalized estimating equations (GEEs) provides consistent estimates of the regression parameters in a marginal regression model for longitudinal data, even when the working correlation model is misspecified (Liang and Zeger, 1986). However, the efficiency of a GEE estimate can be seriously affected by the choice of the working correlation model. This study addresses this problem by proposing a hybrid method that combines multiple GEEs based on different working correlation models, using the empirical likelihood method (Qin and Lawless, 1994). Analyses show that this hybrid method is more efficient than a GEE using a misspecified working correlation model. …

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