JSM 2011 Online Program

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Abstract Details

Activity Number: 308
Type: Contributed
Date/Time: Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract - #301370
Title: Partially Linear Modeling for Conditional Quantile
Author(s): Chaojiang Wu*+ and Yan Yu
Companies: University of Cincinnati and University of Cincinnati
Address: Room 534, 2925 Campus Green Drive, Cincinnati, OH, 45220,
Keywords: Additive Models ; Dimension Reduction ; Penalized Splines ; Single-Index Models ; Smoothing Parameter ; Semiparametric Model
Abstract:

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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