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Abstract Details
Activity Number:
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63
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Type:
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Topic Contributed
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Date/Time:
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Sunday, July 29, 2012 : 4:00 PM to 5:50 AM
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Sponsor:
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ENAR
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Abstract - #304131 |
Title:
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Joint Asymptotics and Inferences for Seminonparametric Models
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Author(s):
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Zuofeng Shang and Guang Cheng*+
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Companies:
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University of Notre Dame and Purdue University
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Address:
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250 N. University Street, West Lafayette, IN, 47907-2066, United States
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Keywords:
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Joint Asymptotics ;
Partly Linear Models ;
Likelihood Ratio Testing ;
Semiparametric Efficiency ;
Smoothing Spline ;
Concentration Inequality
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Abstract:
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In this talk, we consider the joint asymptotics and inferences for the semi-nonparametric models where the Euclidean parameter and an infinite dimensional parameter are both of interest. Within the general partly linear framework, we derive the joint limit distribution for the two parameters which are rescaled according to different convergence rates. The marginal limit distribution for the Euclidean estimate coincides with that derived in the semiparametric literature. To construct the joint confidence region, we propose the likelihood ratio testing approach that can effectively avoid estimating the asymptotic covariance. The employed regularization tool is the smoothing spline. The undersmoothing of the smoothing spline estimate is required for obtaining the valid joint inferences. The key technical tool is a concentration inequality. A by-product result is the marginal asymptotics for the infinite dimensional parameter that are new even in the nonparametric literature.
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Authors who are presenting talks have a * after their name.
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