Graph-Assisted Inverse Regression for Count Data and Its Application to Sequencing Data

DOI10.1080/10618600.2019.1705309OpenAlexW2996413529MaRDI QIDQ5065992

Tao Wang

Publication date: 28 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10618600.2019.1705309

zbMATH Keywords

distributed computingsufficient dimension reductionmodel-based inverse regressionneighborhood selectionconditional independence model

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• glmnet

Cites Work

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• Distributed multinomial regression
• The Adaptive Lasso and Its Oracle Properties
• Fisher lecture: Dimension reduction in regression
• Estimating the dimension of a model
• Testing predictor contributions in sufficient dimension reduction.
• High-dimensional graphs and variable selection with the Lasso
• Simultaneous Equations and Canonical Correlation Theory
• Sufficient dimension reduction and prediction in regression
• Sufficient dimension reduction through discretization-expectation estimation
• Sliced Inverse Regression for Dimension Reduction
• Multivariate Dispersion Models Generated From Gaussian Copula
• The multivariate Poisson-log normal distribution
• Likelihood-Based Sufficient Dimension Reduction
• The LZIP: A Bayesian latent factor model for correlated zero‐inflated counts
• Model Selection and Estimation in Regression with Grouped Variables
• On Sliced Inverse Regression With High-Dimensional Covariates
• Comment
• Multinomial Inverse Regression for Text Analysis

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