A general framework of nonparametric feature selection in high‐dimensional data-Reference-Cited by-同舟云学术

A general framework of nonparametric feature selection in high‐dimensional data

Published:2022-04-07 Issue: Volume: Page:
ISSN:0006-341X
Container-title:Biometrics
language:en
Short-container-title:Biometrics

Author:

Yu Hang¹,Wang Yuanjia²^ORCID,Zeng Donglin³^ORCID

Affiliation:

1. Department of Statistics and Operation Research University of North Carolina at Chapel Hill Chapel Hill North Carolina USA

2. Department of Biostatistics Columbia University New York, New York USA

3. Department of Biostatistics University of North Carolina at Chapel Hill Chapel Hill North Carolina USA

Funder

National Institutes of Health

Publisher

Wiley

Subject

Applied Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.13664

Reference27 articles.

1. Automatic Feature Selection via Weighted Kernels and Regularization

2. Theory of reproducing kernels

3. Homozygosity mapping with SNP arrays identifies TRIM32, an E3 ubiquitin ligase, as a Bardet-Biedl syndrome gene (BBS11)

4. Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models

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