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| Efficient Quantile Estimation for Functional-Coefficient Partially Linear Regression Models |
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Citation： |
Zhangong ZHOU,Rong JIANG,Weimin QIAN.Efficient Quantile Estimation for Functional-Coefficient Partially Linear Regression Models[J].Chinese Annals of Mathematics B,2011,32(5):729~740 |
| Page view： 1900
Net amount： 1693 |
Authors： |
Zhangong ZHOU; Rong JIANG; Weimin QIAN; |
Foundation： |
the Zhejiang Provincial Natural Science Foundation of China (No. Y6110662). |
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| Abstract： |
The quantile estimation methods are proposed for functional-coefficient partially
linear regression (FCPLR) model by combining nonparametric and functional-coeffici-ent
regression (FCR) model. The local linear scheme and the integrated method are used to
obtain local quantile estimators of all unknown functions in the FCPLR model. These
resulting estimators are asymptotically normal, but each of them has big variance. To
reduce variances of these quantile estimators, the one-step backfitting technique is used
to obtain the efficient quantile estimators of all unknown functions, and their asymptotic
normalities are derived. Two simulated examples are carried out to illustrate the proposed
estimation methodology. |
Keywords： |
Functional-coefficient model, Quantile regression, Local linear method,
Backfitting technique, Asymptotic normality |
Classification： |
62J05, 62G08, 62E20 |
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