Penalized Principal Logistic Regression for Sparse Sufficient Dimension Reduction

来源 :上海交通大学 | 被引量 : 0次 | 上传用户：ldjlovell

【摘要】

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【作者】

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Seung Jun Shin Andreas Artemiou

【机构】

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Korea University and Cardiff University

【出处】

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上海交通大学

【发表日期】

：

2016年4期

【关键词】

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Max-SCAD penalty Principal logistic regression Sparse sufficient dimension reduc

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　　The sufficient dimension reduction(SDR)is a successive tool for reducing the dimensionality of predictors by finding the central subspace,a minimal subspace of predictors that preserves all the regression information.When the predictor dimension is large,it is often assumed that only a small number of predictors is informative.

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Penalized Principal Logistic Regression for Sparse Sufficient Dimension Reduction

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