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Activity Number:
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154
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 3, 2009 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Survey Research Methods
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| Abstract - #304077 |
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Title:
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Empirical Likelihood--Based Calibration Methods for Missing Data Problems
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Author(s):
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Jing Qin*+
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Companies:
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National Institute of Allergy and Infectious Diseases
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Address:
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6700B Rockledge Drive, MSC 7609, Bethesda, MD, MD20892,
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Keywords:
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Calibration ; Empirical likelihood ; Missing data ; Survey sampling
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Abstract:
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Calibration estimation has been developed into an important field of research in survey sampling. It is now an indispensable methodological instrument in the production of statistics. A few national statistical agencies have developed software designed to compute calibrated weights based on auxiliary information available in population registers and other sources. However its application in general statistics outside of survey sampling is limited. In this talk, we will demonstrate that the simple calibration method is a powerful tool to handle the general missing data problem when the parameters of interest are defined by unbiased estimating equations. In contrast to the traditional calibration method, the calibration weights depend on the unknown parameters of interest and must be estimated by the calibration estimating equations. Large sample results and simulations are included.
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