Three approaches to supervised learning for compositional data with pairwise logratios
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Publication:6067828
DOI10.1080/02664763.2022.2108007arXiv2111.08953MaRDI QIDQ6067828
Michael J. Greenacre, Germá Coenders
Publication date: 14 December 2023
Published in: Journal of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2111.08953
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- Variable selection in regression with compositional covariates
- Regression analysis for microbiome compositional data
- Covariance-based variable selection for compositional data
- Isometric logratio transformations for compositional data analysis
- New perspectives on water chemistry and compositional data analysis
- Groups of parts and their balances in compositional data analysis
- Analysing pairwise logratios revisited
- Robust logistic zero-sum regression for microbiome compositional data
- Variable selection in compositional data analysis using pairwise logratios
- Structured subcomposition selection in regression and its application to microbiome data analysis
- An Introduction to Statistical Learning
- On Interpretations of tests and effect sizes in regression models with a compositional predictor
- Bayesian-multiplicative treatment of count zeros in compositional data sets
- When relative and absolute information matter: Compositional predictor with a total in generalized linear models
- Generalized linear models with linear constraints for microbiome compositional data
- Log‐ratio lasso: Scalable, sparse estimation for log‐ratio models
- It's All Relative: Regression Analysis with Compositional Predictors
- Bayesian compositional regression with structured priors for microbiome feature selection
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