ArticleStatistics in medicine2015
Using distance covariance for improved variable selection with application to learning genetic risk models.
Article in Statistics in medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- PAM clustering algorithm based on mutual information matrix for ATR-FTIR spectral feature selection and disease diagnosis.BMC medical research methodology · 2025Article
- ASYMPTOTIC DISTRIBUTIONS OF HIGH-DIMENSIONAL DISTANCE CORRELATION INFERENCE.Annals of statistics · 2021Article
- Detecting PCOS susceptibility loci from genome-wide association studies via iterative trend correlation based feature screening.BMC bioinformatics · 2020Article
- Association Factor for Identifying Linear and Nonlinear Correlations in Noisy Conditions.Entropy (Basel, Switzerland) · 2020Article
- An adaptive threshold determination method of feature screening for genomic selection.BMC bioinformatics · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Variable selection is of increasing importance to address the difficulties of high dimensionality in many scientific areas. In this paper, we demonstrate a property for distance covariance, which is incorporated in a novel feature screening procedure together with the use of distance correlation. The approach makes no distributional assumptions for the variables and does not require the specification of a regression model and hence is especially attractive in variable selection given an enormous number of candidate attributes without much information about the true model with the response. The method is applied to two genetic risk problems, where issues including uncertainty of variable selection via cross validation, subgroup of hard-to-classify cases, and the application of a reject option are discussed.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.