This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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3
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Type:
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Invited
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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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General Methodology
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Abstract - #306074 |
Title:
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Adaptive Confidence Intervals for Regression Coefficients in Q-Learning
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Author(s):
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Eric B. Laber and Min Qian and Susan Murphy*+
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Companies:
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University of Michigan and University of Michigan and University of Michigan
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Address:
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439 West Hall, 1085 S. Univ., Ann Arbor, MI, 48109-1107,
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Keywords:
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dynamic treatment regimes ;
adaptive treatment strategies ;
reinforcement learning ;
non-regular
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Abstract:
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Dynamic treatment regimes (or treatment policies) are used to operationalize multi-stage decision making in the medical field. Common approaches to constructing the dynamic treatment regimes from data, such as Q-Learning, employ non-smooth functionals of the data. The non-smoothness leads to non-regular asymptotics under certain generative models. Methods that ignore the non-regularity have poor performance in small samples. In this talk, we present a bootstrap based method for constructing asymptotically valid confidence sets. This method is adaptive in the sense that it provides exact coverage when the true underlying generative model leads to regular asymptotics and is conservative otherwise. Empirical studies show that the amount of conservatism is small.
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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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