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分组数据分位数回归模型的变量选择和估计

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分组数据分位数回归模型的变量选择和估计 刘栋1, 杨冬梅2, 何勇3, 张新生41. 上海财经大学统计与管理学院, 上海 200433;
2. 山东财经大学统计学院, 济南 250014;
3. 山东大学中泰证券金融研究院, 济南 250100;
4. 复旦大学管理学院, 上海 200433 Variable Selection and Estimation of Quantile Regression Model for Stratified Data LIU Dong1, YANG Dongmei2, HE Yong3, HE Yong41. School of statistics and management, Shanghai University of Finance and Economics, Shanghai 200433, China;
2. School of Statistics, Shandong University of Finance and Economics, Jinan 250014, China;
3. Zhongtai Securities Institute for Financial Studies, Shandong University, Jinan 250100, China;
4. School of management, Fudan University, Shanghai 200433, China
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摘要本文主要研究分组数据分位数回归模型的变量选择和估计问题.为了充分反映数据的分组信息,需要假定每组数据的回归系数可以分解成共性部分和分组后的个性部分.为了进行变量筛选,本文提出分解系数的Lasso估计,并进一步提出了自适应Lasso估计.在处理相应优化问题时,采用了变换观测矩阵的方法简化问题求解.本文给出了自适应Lasso估计的Oracle性质证明,并且通过数值模拟研究展示了所提方法的有限样本表现.最后,将此方法应用到乳腺浸润癌致病基因的变量筛选上来展示所提方法的实际应用表现.
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收稿日期: 2020-02-25
PACS:O212.7
基金资助:国家自然科学基金(11801316,11971116),山东省社科规划项目(19BYSJ23,20DTJJ02)资助项目,山东省研究生教育创新项目优质课程《中级计量经济学》(SDYKC18038).

引用本文:
刘栋, 杨冬梅, 何勇, 张新生. 分组数据分位数回归模型的变量选择和估计[J]. 应用数学学报, 2021, 44(5): 722-739. LIU Dong, YANG Dongmei, HE Yong, HE Yong. Variable Selection and Estimation of Quantile Regression Model for Stratified Data. Acta Mathematicae Applicatae Sinica, 2021, 44(5): 722-739.
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http://123.57.41.99/jweb_yysxxb/CN/ http://123.57.41.99/jweb_yysxxb/CN/Y2021/V44/I5/722


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