Dimension reduction in binary response regression: a joint modeling approach
type: Item
instance of: scholarly article
title: Dimension reduction in binary response regression: a joint modeling approach
author: Tao Wang
author: Jun-lan Li
published in: Computational Statistics and Data Analysis
publication date: 2021-05-07T00:00:00Z
Mathematics Subject Classification ID: 62-XX
zbMATH DE Number: 7345818
zbMATH Keywords: binary classification
zbMATH Keywords: joint reduction
zbMATH Keywords: latent variable modeling
zbMATH Keywords: model-based inverse regression
zbMATH Keywords: sufficient dimension reduction
MaRDI profile type: Publication
full work available at URL: j.csda.2020.107131
OpenAlex ID: W3096365922
cites work: Estimating the structural dimension of regressions via parametric inverse regression
cites work: Sufficient Reductions in Regressions With Elliptically Contoured Inverse Predictors
cites work: Q4217298
cites work: Fisher lecture: Dimension reduction in regression
cites work: Principal fitted components for dimension reduction in regression
cites work: Likelihood-Based Sufficient Dimension Reduction
cites work: Estimating sufficient reductions of the predictors in abundant high-dimensional regressions
cites work: Dimension Reduction in Binary Response Regression
cites work: Comment
cites work: Theory & Methods: Special Invited Paper: Dimension Reduction and Visualization in Discriminant Analysis (with discussion)
cites work: Q4139463
cites work: Supervised dimension reduction for ordinal predictors
cites work: Q5251797
cites work: Sliced Inverse Regression for Dimension Reduction
cites work: Q4642510
cites work: Principal support vector machines for linear and nonlinear sufficient dimension reduction
cites work: On Directional Regression for Dimension Reduction
cites work: Support vector machines for classification in nonstandard situations
cites work: Estimating the dimension of a model
cites work: Probability-enhanced sufficient dimension reduction for binary classification
cites work: Principal weighted support vector machines for sufficient dimension reduction in binary classification
cites work: Q4821813
cites work: Prediction analysis for microbiome sequencing data
cites work: Q4969067
DOI: 10.1016/J.CSDA.2020.107131
Recommended article: Q4844385
Recommended article: Dimension reduction for functional regression with a binary response
Recommended article: Q3580597
Recommended article: A joint regression modeling framework for analyzing bivariate binary data in \(\mathsf{R}\)
Recommended article: Response dimension reduction: model-based approach
Recommended article: Response dimension reduction for the conditional mean in multivariate regression
Recommended article: Response shrinkage estimators in binary regression
Recommended article: A selective review of sufficient dimension reduction for multivariate response regression
description: scientific article; zbMATH DE number 7345818