This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
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Activity Number:
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36
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Type:
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Contributed
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Date/Time:
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Sunday, August 1, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #307659 |
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Title:
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Quantile Regression with the Presence of Missing Covariates
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Author(s):
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Yunwen Yang* and Ying Wei+
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Companies:
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University of Illinois and Columbia University
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Address:
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722 West 168th St. Rm 644, New York City, NY, 10032,
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Keywords:
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Missing covariates ;
quantile regression
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Abstract:
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Regression quantiles can be underpowered or substantially biased when some covariates are missing. We propose a new method that produces consistent linear quantile estimation in the presence of missing covariates. The method corrects the induced bias by constructing unbiased estimating equations that utilize all the available data, and simultaneously hold for all the quantile levels. An iterative EM-type estimation algorithm to obtain the solutions to such joint estimation equations is provided. The finite sample performance of the proposed method is investigated in a simulation study. Finally, we apply our methodology to part of the data from National Health and Nutrition Examination Survey.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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