Activity Number:
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201
- Nonparametric Statistics Student Paper Competition Presentations
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Type:
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Topic-Contributed
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Date/Time:
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Tuesday, August 10, 2021 : 1:30 PM to 3:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #317169
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Title:
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Model-Free Conditional Feature Screening with FDR Control
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Author(s):
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Zhaoxue Neve Tong* and Zhanrui Cai and Runze Li
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Companies:
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The Pennsylvania State University and The Pennsylvania State University and Pennsylvania State University
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Keywords:
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false discovery rate control;
ranking consistency;
sure screening;
ultra-high dimensional data analysis
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Abstract:
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In this paper, we propose a model-free conditional feature screening method with false discovery rate (FDR) control for ultra-high dimensional data. The proposed method is built upon a new measure of conditional independence. Thus, the new method does not require a specific functional form of regression function and is robust to heavy-tailed response and predictors. The variables to be conditional on are allowed to be multivariate. The proposed method enjoys sure screening and ranking consistency properties under mild regularity conditions. To control the FDR, we apply the Reflection via Data Splitting method and prove its theoretical guarantee using novel martingale theory and empirical process techniques. Simulated examples and real data analysis show that the proposed method performs very well compared with existing works.
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Authors who are presenting talks have a * after their name.