Inverse regression approach to robust nonlinear high-to-low dimensional mapping

DOI10.1016/j.jmva.2017.09.009zbMath1408.62119OpenAlexW2602574842MaRDI QIDQ1686148

Antoine Deleforge, Florence Forbes, Émeline Perthame

Publication date: 21 December 2017

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://hal.inria.fr/hal-01347455/file/JMVA-17-48-EiC-REV3.pdf

zbMATH Keywords

EM algorithmnonlinear regressionrobust regressionhigh dimensioninverse regressionStudent distributionmixture of regressions

Mathematics Subject Classification ID

Nonparametric robustness (62G35) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Characterization and structure theory for multivariate probability distributions; copulas (62H05) General nonlinear regression (62J02)

Related Items (6)

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Cites Work

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• A new family of multivariate heavy-tailed distributions with variable marginal amounts of tailweight: application to robust clustering
• High-dimensional regression with Gaussian mixtures and partially-latent response variables
• Local statistical modeling via a cluster-weighted approach with elliptical distributions
• Robust modeling using non-elliptically contoured multivariate \(t\) distributions
• Bayesian robust inference of sample selection using selection-\(t\) models
• Fisher lecture: Dimension reduction in regression
• Robust estimation of mixtures of regressions with random covariates, via trimming and constraints
• \(\ell_{1}\)-penalization for mixture regression models
• Mixtures of linear regressions
• Robust Bayesian clustering
• Finite mixtures of multivariate skew \(t\)-distributions: some recent and new results
• Cluster-weighted \(t\)-factor analyzers for robust model-based clustering and dimension reduction
• Finite mixture regression: a sparse variable selection by model selection for clustering
• High-dimensional data clustering
• Multivariate adaptive regression splines
• A Markov model for switching regressions
• Identifiability of models for clusterwise linear regression
• Robust mixture regression using the \(t\)-distribution
• Clustering with the multivariate normal inverse Gaussian distribution
• Location and scale mixtures of Gaussians with flexible tail behaviour: properties, inference and application to multivariate clustering
• Clustering and classification via cluster-weighted factor analyzers
• Finite mixture and Markov switching models.
• Sufficient dimension reduction and prediction in regression
• Sliced Inverse Regression for Dimension Reduction
• 10.1162/15324430152748236
• Multivariate T-Distributions and Their Applications
• A Heckman Selection-tModel
• On the Conditional Distribution of the Multivariate t Distribution
• Random forests

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