Model-based SIR for dimension reduction

DOI10.1016/j.csda.2011.05.006zbMath1218.62037arXiv1508.02186OpenAlexW2050083787MaRDI QIDQ2275652

Luca Scrucca

Publication date: 9 August 2011

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

Full work available at URL: https://arxiv.org/abs/1508.02186

zbMATH Keywords

mixture modelingsliced inverse regressionsummary plots

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Estimation in multivariate analysis (62H12)

Related Items (9)

Entropy-based sliced inverse regression ⋮ A new sliced inverse regression method for multivariate response ⋮ A robust proposal of estimation for the sufficient dimension reduction problem ⋮ Sufficient dimension reduction for clustered data via finite mixture modelling ⋮ Using DAGs to identify the sufficient dimension reduction in the principal fitted components model ⋮ Tail dimension reduction for extreme quantile estimation ⋮ Nonparametric confidence intervals for conditional quantiles with large-dimensional covariates ⋮ Modal Principal Component Analysis ⋮ On central matrix based methods in dimension reduction

Uses Software

• mclust
• R
• alr3
• Mixmod

Cites Work

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• Finite mixture models
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• Likelihood-Based Sufficient Dimension Reduction
• Direction estimation in single-index regressions
• Sufficient Dimension Reduction via Inverse Regression
• On Sliced Inverse Regression With High-Dimensional Covariates
• Matrix Algebra
• Comment

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