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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/80623


    Title: Structured variable selection via prior-induced hierarchical penalty functions
    Authors: Yen, Tso-Jung;Yen, Yu-Min
    顏佑銘
    Contributors: 國貿系
    Keywords: Group sparsity;Spike and slab priors;Log-sum approximation to the l0l0-norm;Majorization–minimization algorithms;Alternating direction method of multipliers
    Date: 2016-04
    Issue Date: 2016-01-15 15:44:10 (UTC+8)
    Abstract: 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.
    Relation: Computational Statistics & Data Analysis, 96, 87-103
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1016/j.csda.2015.10.011
    DOI: 10.1016/j.csda.2015.10.011
    Appears in Collections:[國際經營與貿易學系 ] 期刊論文

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