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Advanced Economic Seminars 226: Joint feature screening for ultra-high-dimensional sparse additive hazards model by the sparsity-restricted pseudo-score estimator

Publisher:   Time:2018-11-28 14:12:31

Topic: Joint feature screening for ultra-high-dimensional sparse additive hazards model by the sparsity-restricted pseudo-score estimator

Lecturer: Chen Xiaolin, School of Statistics, Qufu Normal University

Time: 16:15-17:00 p.m. 1st of November 2018Thursday

Venue:B219, Zhixin Building

Abstract: Due to the coexistence of ultra-high dimensionality and right censoring, it is very challenging to develop feature screening procedure for ultra-high-dimensional survival data. In this talk, I will present a joint screening approach for the sparse additive hazards model with ultra-high-dimensional features. This method is based on a sparsity-restricted pseudo-score estimator which could be obtained effectively through the iterative hard-thresholding algorithm. My coauthors and I have established the sure screening property of the proposed procedure theoretically under rather mild assumptions. Extensive simulation studies verify its improvements over the main existing screening approaches for ultra-high-dimensional survival data. Finally, the proposed screening method is illustrated by dataset from a breast cancer study.

Last:Advanced Economic Seminars 227:  Alaska's Reindeer Games:  Assimilation and Economic Development Next:Advanced Economic Seminars 225: Rank-based Canonical Correlation and Its Application in Feature Screening for a Large Class of Semi-parametric Regression Models

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