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Abstract Details
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Activity Number:
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29
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Type:
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Contributed
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Date/Time:
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Sunday, July 29, 2012 : 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 - #305226 |
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Title:
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Parametrically Guided Estimation for Varying Coefficient Models
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Author(s):
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Clemontina A. Davenport*+ and Yichao Wu and Arnab Maity
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Companies:
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North Carolina State University and North Carolina State University and North Carolina State University
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Address:
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2608 Charenson Pl, Raleigh, NC, 27614, United States
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Keywords:
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generalized linear models ;
guided estimation ;
local polynomial smoothing ;
nonparametric regression ;
varying coefficient model
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Abstract:
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Varying coefficient models allow us to generalize standard linear regression models to incorporate complex covariate effects by modeling the regression coefficients as functions of another covariate. For nonparametric varying coefficients, we can borrow the idea of parametrically guided estimation to improve asymptotic bias. In this paper, we develop a guided estimation procedure for the nonparametric varying coefficient models. Asymptotic properties are established for the guided estimators and a method of bandwidth selection via bias-variance tradeoff is proposed. We compare the performance of the guided estimator with that of the unguided estimator via both simulation and real data examples.
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