Sequential Feature Screening for Generalized Linear Models with Sparse Ultra-High Dimensional Data

ZHANG Junying · WANG Hang · ZHANG Riquan · ZHANG Jiajia

系统科学与复杂性(英文) ›› 2020, Vol. 33 ›› Issue (2) : 510-526.

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PDF(244 KB)
系统科学与复杂性(英文) ›› 2020, Vol. 33 ›› Issue (2) : 510-526. DOI: 10.1007/s11424-020-8273-2

Sequential Feature Screening for Generalized Linear Models with Sparse Ultra-High Dimensional Data

    ZHANG Junying · WANG Hang · ZHANG Riquan · ZHANG Jiajia
作者信息 +

Sequential Feature Screening for Generalized Linear Models with Sparse Ultra-High Dimensional Data

    ZHANG Junying · WANG Hang · ZHANG Riquan · ZHANG Jiajia
Author information +
文章历史 +

Abstract

This paper considers the iterative sequential lasso (ISLasso) variable selection for generalized linear model with ultrahigh dimensional feature space. The ISLasso selects features by estimated parameter sequentially iteratively for the second order approximation of likelihood function where the features selected depend on regulatory parameters. The procedure stops when extended BIC (EBIC) reaches a minimum. Simulation study demonstrates that the new method is a desirable approach over other methods.

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ZHANG Junying · WANG Hang · ZHANG Riquan · ZHANG Jiajia. Sequential Feature Screening for Generalized Linear Models with Sparse Ultra-High Dimensional Data. 系统科学与复杂性(英文), 2020, 33(2): 510-526 https://doi.org/10.1007/s11424-020-8273-2
ZHANG Junying · WANG Hang · ZHANG Riquan · ZHANG Jiajia. Sequential Feature Screening for Generalized Linear Models with Sparse Ultra-High Dimensional Data. Journal of Systems Science and Complexity, 2020, 33(2): 510-526 https://doi.org/10.1007/s11424-020-8273-2
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