.png){kind=icon}
返回
Variable selection and inference strategies for multiple compositional regression
.png){kind=icon}
PRE
AI
DOI：10.1016/j.chemolab.2024.105121.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
An important problem in compositional data analysis is variable selection in linear regression models with compositional covariates. In the context of microbiome data analysis, there is a demand for considering grouping information such as structures among taxa and multiple sampling sites, resulting in multiple compositional covariates. We develop and compare two different methods of variable selection and inference strategies, based on the debiased lasso and a resampling-based approach. Confidence intervals for individual regression coefficients, obtained from each of the two methods, are shown to be asymptotically valid even in a high -dimension, low -sample -size regime. However, microbiome data often have extremely small sample sizes, rendering asymptotic results unreliable. Through extensive numerical comparisons of the finite -sample performances of the two methods, we find that resampling-based approaches outperform the debiased compositional lasso in cases of extremely small sample sizes, showing higher positive predictive values. Conversely, for larger sample sizes, debiasing yields better results. We apply the proposed multiple compositional regression to steer microbiome data, identifying key bacterial taxa associated with important cattle quality measures.
Keyword:
Variables selection
Multiple compositional data
Microbiome data
Lasso estimation
Debiasing
Resampling-based inference
期刊
IF:
3.8
论文数:
4.6K
被引数:
1.2W
机构
引用论文
Artificial Scaffold PolypeptidesAs an Efficient Tool for the Targeted Delivery of Nanostructures In Vitro and In Vivo.png)
Acta Naturae
OAAI
Acta NaturaeIF0
OAAI分享收藏分享收藏
In Defense of the Indefensible: A Very Naive Approach to High-Dimensional Inference捍卫不可辩护: 一种非常幼稚的高维推理方法STATISTICAL SCIENCEOAAI
STATISTICAL SCIENCEIF3.4
OAAI分享收藏
查看更多内容