کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1145369 | 1489659 | 2015 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Penalized regression across multiple quantiles under random censoring
ترجمه فارسی عنوان
رگرسیون مجازی در بین کانالهای متعدد تحت سانسور تصادفی
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
آنالیز عددی
چکیده انگلیسی
In quantile regression, it is of interest to determine whether a covariate has varying or constant effect across quantiles, since in situations where the quantile coefficients share some common features we can improve the estimation efficiency through joint modeling of multiple quantiles. To automatically perform estimation and detection of the interquantile commonality, we propose a new penalization procedure with two variations of interquantile penalties for censored quantile regression. The proposed methods are shown to be consistent in separating the constant and varying effects across quantiles, and the resulting slope estimators have the same asymptotic efficiency with the oracle estimators obtained as if the true interquantile model structure is known a priori. Our simulation study suggests that the proposed estimators have competitive or higher efficiency than the existing estimator obtained by fitting censored quantile regression at each quintile level separately. The practical value of the proposed methods is further illustrated through the analysis of a renal disease data.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Multivariate Analysis - Volume 141, October 2015, Pages 132-146
Journal: Journal of Multivariate Analysis - Volume 141, October 2015, Pages 132-146
نویسندگان
Yanlin Tang, Huixia Judy Wang,