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Abstract Details
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Activity Number:
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343
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #300903 |
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Title:
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Regression Models with Covariates Missing in Nonmonotonic Patterns
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Author(s):
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Yang Zhao*+ and Meng Liu
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Companies:
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University of Regina and University of Regina
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Address:
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Department of Mathematics and Statistics, Regina, SK, S4S 0A2 , Canada
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Keywords:
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Complete-case analysis ;
Estimating equation ;
Nonmonotonic missing patterns ;
Parametric working model ;
Weighted complete-case analysis
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Abstract:
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This research proposes estimation methods for regression models with covariances missing in arbitrary nonmonotonic patterns. It explains the idea of using a sequence of parametric working models to extract information from incomplete observations and computing efficient estimates of regression parameters. The proposal is general and can be applied to analysis for regression models with arbitrary nonmonotonic missing data patterns. We use simulation studies and an analysis of a real data example to illustrate the proposed method.
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