政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/80623

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政大機構典藏 > 商學院 > 國際經營與貿易學系 > 期刊論文 > Item 140.119/80623

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題名:	Structured variable selection via prior-induced hierarchical penalty functions
作者:	Yen, Tso-Jung;Yen, Yu-Min 顏佑銘
貢獻者:	國貿系
關鍵詞:	Group sparsity;Spike and slab priors;Log-sum approximation to the l0l0-norm;Majorization–minimization algorithms;Alternating direction method of multipliers
日期:	2016-04
上傳時間:	2016-01-15 15:44:10 (UTC+8)
摘要:	The paper studies a grouped variable selection problem in a linear regression setting by proposing a hierarchical penalty function to model collective behavior of the regression coefficients. This hierarchical penalty function consists of two levels. At the top level, it models the group effect of covariates by introducing an index function on the event that the l 2 -norm of the corresponding regression coefficients is not equal to zero. At the bottom level, it models the individual effect of a covariate with an index function on the event that the corresponding regression coefficient is not equal to zero. Under this hierarchical penalty function, model estimation can be conducted by applying an iteration-based numerical procedure to solve a sequence of modified optimization problems. Simulation study shows that the proposed estimator performs relatively well when the number of covariates exceeds the sample size, and when both the true and false covariates are included in the same group. Theoretical analysis suggests that the l 2 estimation error of the proposed estimator can achieve a good upper bound if some regularity conditions are satisfied.
關聯:	Computational Statistics & Data Analysis, 96, 87-103
資料類型:	article
DOI 連結:	http://dx.doi.org/10.1016/j.csda.2015.10.011
DOI:	10.1016/j.csda.2015.10.011
顯示於類別:	[國際經營與貿易學系 ] 期刊論文

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