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Abstract Details
Activity Number:
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308
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Business and Economic Statistics Section
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Abstract - #301370 |
Title:
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Partially Linear Modeling for Conditional Quantile
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Author(s):
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Chaojiang Wu*+ and Yan Yu
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Companies:
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University of Cincinnati and University of Cincinnati
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Address:
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Room 534, 2925 Campus Green Drive, Cincinnati, OH, 45220,
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Keywords:
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Additive Models ;
Dimension Reduction ;
Penalized Splines ;
Single-Index Models ;
Smoothing Parameter ;
Semiparametric Model
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Abstract:
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We consider the estimation problem of conditional quantile when high demensional covariates are involved. To overcome the "curse of dimensionality" yet retain model flexibility, we propose to two partially linear models for conditional quantile: partially linear single-index models (QPLSIM) and partially linear additive models (QPLAM). The unknown functions are estimated by penalized splines. An iteratively reweighted least square algorithm is developed. To facilitate model comparisons, we develop effective model degrees of freedom as the measure of model complexity for penalized spline conditional quantile. Two smoothing parameter selection criteria, Generalized Approximate Cross-validation (GACV) and Schwartz-type Information Criterion (SIC) are studied. Some asymptotic properties are established. Finite sample properties are studied by simulation studies. A real data application demonstrates the success of proposed approach. Both simulations and real applications show encouraging results of our estimators.
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