SCAD-Ridge penalized likelihood estimators for ultra-high dimensional models
SCAD-Ridge penalized likelihood estimators for ultra-high dimensional models
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超高维模型的 SCAD-Ridge 惩罚似然估计器
DOI：
10.15672/hjms.201612518375
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发表时间：
2016-05
影响因子：
0.8
通讯作者：
Muhammad Amin
中科院分区：
文献类型：
--
作者：
Ying Dong;Lixin Song;Muhammad Amin
从大数据中提取尽可能多的信息是现代统计数据中的一个燃烧问题，因为与观察相比，已采用了惩罚来解决此类问题。许多成就有Alre
Extraction of as much information as possible from huge data is a burning issue in the modern statistics due to more variables as compared to observations therefore penalization has been employed to resolve that kind of issues. Many achievements have already been made by such penalization techniques. Due to the large number of variables in many research areas declare it a high dimensional problem and with this the sample correlation becomes very large. In this paper, we studied the maximum likelihood estimation of variable selection under smoothly clipped absolute deviation (SCAD) and Ridge penalties with ultra-high dimension settings to solve this problem. We established the oracle property of the proposed model under some conditions by following the theoretical method of Kown and Kim (2012) [19]. These result can greatly broaden the application scope of high-dimension data. Numerical studies are discussed to assess the performance of the proposed method. The SCAD-Ridge given better results than the Lasso, Enet and SCAD.
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DOI：
10.1214/10-aoas388
发表时间：
2011-01-01
期刊：
The annals of applied statistics
影响因子：
--
作者：
Breheny P;Huang J
通讯作者：
Huang J
DOI：
10.1016/0095-0696(78)90006-2
发表时间：
1978-01-01
影响因子：
4.6
作者：
HARRISON, D;RUBINFELD, DL
通讯作者：
RUBINFELD, DL
DOI：
--
发表时间：
2015
期刊：
--
影响因子：
--
作者：
Muhammad Amin;Lixin Song;Milton Abdul Thorlie;Xiaoguang Wang
通讯作者：
Muhammad Amin;Lixin Song;Milton Abdul Thorlie;Xiaoguang Wang
影响因子：
2.5
作者：
A. E. Hoerl;R. Kennard
通讯作者：
A. E. Hoerl;R. Kennard
影响因子：
4.5
作者：
Fan, JQ;Peng, H
通讯作者：
Peng, H