Variable selection in regression using maximal correlation and distance correlation

DOI10.1080/00949655.2014.895354zbMath1457.62190OpenAlexW2021420335MaRDI QIDQ5220819

Maria L. Rizzo, C. Deniz Yenigün

Publication date: 27 March 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/11693/24125

zbMATH Keywords

nonlinear regressionvariable selectiondistance correlationmaximal correlation

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Applications of statistics to actuarial sciences and financial mathematics (62P05) Measures of association (correlation, canonical correlation, etc.) (62H20)

Related Items (4)

Variable selection in functional additive regression models ⋮ An Updated Literature Review of Distance Correlation and Its Applications to Time Series ⋮ An RKHS model for variable selection in functional linear regression ⋮ Feature selection based on distance correlation: a filter algorithm

Uses Software

• R
• energy
• acepack

Cites Work

• Measuring and testing dependence by correlation of distances
• The Adaptive Lasso and Its Oracle Properties
• Distance covariance in metric spaces
• Energy statistics: a class of statistics based on distances
• The affinely invariant distance correlation
• Brownian distance covariance
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• Estimating the dimension of a model
• On Gebelein's correlation coefficient
• Least angle regression. (With discussion)
• On the uniqueness of distance covariance
• A Test of Independence in Two-Way Contingency Tables Based on Maximal Correlation
• On measures of dependence
• A Selective Overview of Variable Selection in High Dimensional Feature Space (Invited Review Article)
• Estimating Optimal Transformations for Multiple Regression and Correlation
• The asymptotic distribution of the rènyi maximal correlation
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Feature Screening via Distance Correlation Learning
• Regularization and Variable Selection Via the Elastic Net
• Some Comments on C P
• Remarks on the maximum correlation coefficient
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