Using distance covariance for improved variable selection with application to learning genetic risk models

By Jing Kong, Sijian Wang, Grace Wahba. 2014

43 citations recorded by the source corpus.

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. Copyright © 2015 John Wiley & Sons, Ltd.

Subfields: Genetics, Molecular Biology, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Ahmed M. Elgammal
2. Chetan Tonde
3. Praneeth Vepakomma

In Molecular Biology

1. Ahmed M. Elgammal
2. Chetan Tonde
3. Praneeth Vepakomma

In Statistics and Probability

1. Ahmed M. Elgammal
2. Chetan Tonde
3. Praneeth Vepakomma

Citations in the corpus

• Supervised Dimensionality Reduction via Distance Correlation Maximization (2016, methodology): “To further add, other works of Li et al. [2012], Kong et al. [2015], Berrendero et al. [2014] have used Distance Correlation as a criterion for feature selection in a regression setting.”
• An Updated Literature Review of Distance Correlation and Its Applications to Time Series (2017, unknown): “Kong et al. (2015) have derived an automatic stopping rule for DC-SIS, which determines the number of predictors to select. Recently, Liu and Wang (2017) have proposed a modified version of DC-SIS based on the concept of conditional distance correlation (Wang et al., 2015), which allows for feature ”