MM algorithms for distance covariance based sufficient dimension reduction and sufficient variable selection

DOI10.1016/j.csda.2020.107089OpenAlexW2994926919MaRDI QIDQ829724

N. E. Zubov

Publication date: 6 May 2021

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1912.06342

zbMATH Keywords

variable selectionmajorization-minimizationsufficient dimension reductiondistance covariancemanifold optimizationRiemannian Newton's method

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• Manopt
• GitHub

Cites Work

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• Measuring and testing dependence by correlation of distances
• Sliced Regression for Dimension Reduction
• The Adaptive Lasso and Its Oracle Properties
• An MM Algorithm for Split Feasibility Problems
• Direction estimation in single-index models via distance covariance
• Sufficient dimension reduction based on an ensemble of minimum average variance estimators
• Coordinate-independent sparse sufficient dimension reduction and variable selection
• Dimension reduction based on constrained canonical correlation and variable filtering
• Successive direction extraction for estimating the central subspace in a multiple-index regres\-sion
• Brownian distance covariance
• An integral transform method for estimating the central mean and central subspaces
• Hedonic housing prices and the demand for clean air
• Convex analysis approach to d. c. programming: Theory, algorithms and applications
• A matrix-free implementation of Riemannian Newton's method on the Stiefel manifold
• DC programming and DCA: thirty years of developments
• The DC (Difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems
• Variable selection using MM algorithms
• Manopt, a Matlab toolbox for optimization on manifolds
• Fourier Methods for Estimating the Central Subspace and the Central Mean Subspace in Regression
• On Directional Regression for Dimension Reduction
• Sliced Inverse Regression for Dimension Reduction
• The Geometry of Algorithms with Orthogonality Constraints
• On the Interpretation of Regression Plots
• Graphics for Regressions With a Binary Response
• A D.C. Optimization Algorithm for Solving the Trust-Region Subproblem
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Riemannian Newton-type methods for joint diagonalization on the Stiefel manifold with application to independent component analysis
• An Adaptive Estimation of Dimension Reduction Space
• A Semiparametric Approach to Dimension Reduction
• Likelihood-Based Sufficient Dimension Reduction
• Sequential Sufficient Dimension Reduction for Large p, Small n Problems
• Model Selection and Estimation in Regression with Grouped Variables
• Direction estimation in single-index regressions
• Sufficient Dimension Reduction via Inverse Regression
• Comment

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