Stable prediction in high-dimensional linear models

doi:10.1007/s11222-016-9694-6

CSpace > 应用数学研究所

	Stable prediction in high-dimensional linear models
	Lin, Bingqing 1; Wang, Qihua1,2 ; Zhang, Jun 1; Pang, Zhen 3
	2017-09-01
发表期刊	STATISTICS AND COMPUTING
ISSN	0960-3174
卷号	27 期号:5 页码:1401-1412
摘要	We propose a Random Splitting Model Averaging procedure, RSMA, to achieve stable predictions in high-dimensional linear models. The idea is to use split training data to construct and estimate candidate models and use test data to form a second-level data. The second-level data is used to estimate optimal weights for candidate models by quadratic optimization under non-negative constraints. This procedure has three appealing features: (1) RSMA avoids model overfitting, as a result, gives improved prediction accuracy. (2) By adaptively choosing optimal weights, we obtain more stable predictions via averaging over several candidate models. (3) Based on RSMA, a weighted importance index is proposed to rank the predictors to discriminate relevant predictors from irrelevant ones. Simulation studies and a real data analysis demonstrate that RSMA procedure has excellent predictive performance and the associated weighted importance index could well rank the predictors.
关键词	Model averaging Variable selection Penalized regression Screening
DOI	10.1007/s11222-016-9694-6
语种	英语
资助项目	Natural Science Foundation of Shenzhen University[201542] ; Natural Science Foundation of Shenzhen University[701] ; Natural Science Foundation of Shenzhen University[000360023408] ; National Science Fund for Distinguished Young Scholars in China[10725106] ; National Natural Science Foundation of China[11171331] ; National Natural Science Foundation of China[11331011] ; National Natural Science Foundation of China[11401391] ; Key Lab of Random Complex Structure and Data Science, CAS ; Project of Department of Education of Guangdong Province of China[2014KTSCX112] ; Hong Kong Polytechnic University[G-YBKQ]
WOS研究方向	Computer Science ; Mathematics
WOS类目	Computer Science, Theory & Methods ; Statistics & Probability
WOS记录号	WOS:000400831700016
出版者	SPRINGER
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/25447
专题	应用数学研究所
通讯作者	Zhang, Jun
作者单位	1.Shenzhen Univ, Inst Stat Sci, Coll Math & Stat, Shenzhen 518060, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 3.Hong Kong Polytech Univ, Dept Appl Math, Kowloon, Hong Kong, Peoples R China
推荐引用方式 GB/T 7714	Lin, Bingqing,Wang, Qihua,Zhang, Jun,et al. Stable prediction in high-dimensional linear models[J]. STATISTICS AND COMPUTING,2017,27(5):1401-1412.
APA	Lin, Bingqing,Wang, Qihua,Zhang, Jun,&Pang, Zhen.(2017).Stable prediction in high-dimensional linear models.STATISTICS AND COMPUTING,27(5),1401-1412.
MLA	Lin, Bingqing,et al."Stable prediction in high-dimensional linear models".STATISTICS AND COMPUTING 27.5(2017):1401-1412.