کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1144596 | 957423 | 2014 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Penalized weighted composite quantile regression in the linear regression model with heavy-tailed autocorrelated errors
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
آمار و احتمال
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![عکس صفحه اول مقاله: Penalized weighted composite quantile regression in the linear regression model with heavy-tailed autocorrelated errors Penalized weighted composite quantile regression in the linear regression model with heavy-tailed autocorrelated errors](/preview/png/1144596.png)
چکیده انگلیسی
In this paper, a penalized weighted composite quantile regression estimation procedure is proposed to estimate unknown regression parameters and autoregression coefficients in the linear regression model with heavy-tailed autoregressive errors. Under some conditions, we show that the proposed estimator possesses the oracle properties. In addition, we introduce an iterative algorithm to achieve the proposed optimization problem, and use a data-driven method to choose the tuning parameters. Simulation studies demonstrate that the proposed new estimation method is robust and works much better than the least squares based method when there are outliers in the dataset or the autoregressive error distribution follows heavy-tailed distributions. Moreover, the proposed estimator works comparably to the least squares based estimator when there are no outliers and the error is normal. Finally, we apply the proposed methodology to analyze the electricity demand dataset.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of the Korean Statistical Society - Volume 43, Issue 4, December 2014, Pages 531-543
Journal: Journal of the Korean Statistical Society - Volume 43, Issue 4, December 2014, Pages 531-543
نویسندگان
Yunlu Jiang, Hong Li,