کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1144637 | 957425 | 2013 | 16 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Variable selection of the quantile varying coefficient regression models Variable selection of the quantile varying coefficient regression models](/preview/png/1144637.png)
As a useful supplement to mean regression, quantile regression is a completely distribution-free approach and is more robust to heavy-tailed random errors. In this paper, a variable selection procedure for quantile varying coefficient models is proposed by combining local polynomial smoothing with adaptive group LASSO. With an appropriate selection of tuning parameters by the BIC criterion, the theoretical properties of the new procedure, including consistency in variable selection and the oracle property in estimation, are established. The finite sample performance of the newly proposed method is investigated through simulation studies and the analysis of Boston house price data. Numerical studies confirm that the newly proposed procedure (QKLASSO) has both robustness and efficiency for varying coefficient models irrespective of error distribution, which is a good alternative and necessary supplement to the KLASSO method.
Journal: Journal of the Korean Statistical Society - Volume 42, Issue 3, September 2013, Pages 343–358