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Stable prediction in high-dimensional linear models
Lin, Bingqing1; Wang, Qihua1,2; Zhang, Jun1; Pang, Zhen3
AbstractWe 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.
KeywordModel averaging Variable selection Penalized regression Screening
Funding ProjectNatural 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 Research AreaComputer Science ; Mathematics
WOS SubjectComputer Science, Theory & Methods ; Statistics & Probability
WOS IDWOS:000400831700016
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Document Type期刊论文
Corresponding AuthorZhang, Jun
Affiliation1.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
Recommended Citation
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.
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