Identification of microbial features in multivariate regression under false discovery rate control
From MaRDI portal
Publication:6113732
DOI10.1016/j.csda.2022.107621MaRDI QIDQ6113732
Unnamed Author, Lingzhou Xue, Xiang Zhan
Publication date: 11 July 2023
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
multivariate regressionlog-ratio transformationlogistic-normal distributionknockoff filterfalse discovery rate controlmicrobial feature selection
Cites Work
- Unnamed Item
- Unnamed Item
- Modeling microbial abundances and dysbiosis with beta-binomial regression
- Variable selection in regression with compositional covariates
- The Adaptive Lasso and Its Oracle Properties
- Regression analysis for microbiome compositional data
- The solution path of the generalized lasso
- Controlling the false discovery rate via knockoffs
- False discoveries occur early on the Lasso path
- Testing for differential abundance in compositional counts data, with application to microbiome studies
- Structured subcomposition selection in regression and its application to microbiome data analysis
- Strong oracle optimality of folded concave penalized estimation
- A new approach to Cholesky-based covariance regularization in high dimensions
- Variable selection for high dimensional multivariate outcomes
- Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure
- Logistic-Normal Distributions: Some Properties and Uses
- Statistical Interpretation of Species Composition
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Panning for Gold: ‘Model-X’ Knockoffs for High Dimensional Controlled Variable Selection
- Deep Knockoffs
- The p-filter: Multilayer False Discovery Rate Control for Grouped Hypotheses
- A Logistic Normal Multinomial Regression Model for Microbiome Compositional Data Analysis
- Metropolized Knockoff Sampling
- Compositional knockoff filter for high‐dimensional regression analysis of microbiome data
This page was built for publication: Identification of microbial features in multivariate regression under false discovery rate control
