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Activity Number:
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44
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Type:
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Invited
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Date/Time:
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Sunday, August 3, 2008 : 4:00 PM to 5:50 PM
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Sponsor:
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Memorial
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| Abstract - #300925 |
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Title:
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Using Empirical Likelihood Methods To Obtain Range Restricted
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Author(s):
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Jiahua Chen*+ and J. N. K. Rao and Randy Sitter
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Companies:
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The University of British Columbia and Carleton University and Simon Fraser University
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Address:
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Department of Statistics, Vancouver, BC, V6T 1Z2, Canada
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Keywords:
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Abstract:
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Design weights in surveys are often adjusted to accommodate auxiliary information and meet pre-specified range restrictions, typically via some ad hoc algorithmic adjustment to a generalized regression estimator. We present a simple solution to this problem using empirical likelihood methods or generalized regression. We first develop algorithms for computing empirical likelihood estimators and model-calibrated empirical likelihood estimators. The first algorithm solves the computational problem of the empirical likelihood method in general, both in survey and nonsurvey settings, and theoretically guarantees its convergence. The second exploits properties of the model calibration method and is particularly simple. The algorithms are adapted to handle benchmark constraints and prespecified range restrictions on the weight adjustments.
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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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