8 papers · 1 filter
It's All Relative: New Regression Paradigm for Microbiome Compositional Data
Gen Li, Yan Li, Kun Chen
Microbiome data are complex in nature, involving high dimensionality, compositionally, zero inflation, and taxonomic hierarchy. Compositional data reside in a simplex that does not…
Generalized Co-sparse Factor Regression
Aditya Mishra, Dipak K. Dey, Yong Chen +1
Multivariate regression techniques are commonly applied to explore the associations between large numbers of outcomes and predictors. In real-world applications, the outcomes are o…
Multivariate Log-Contrast Regression with Sub-Compositional Predictors: Testing the Association Between Preterm Infants' Gut Microbiome and Neurobehavioral Outcomes
Xiaokang Liu, Xiaomei Cong, Gen Li +2
The so-called gut-brain axis has stimulated extensive research on microbiomes. One focus is to assess the association between certain clinical outcomes and the relative abundances…
Multivariate Functional Regression via Nested Reduced-Rank Regularization
Xiaokang Liu, Shujie Ma, Kun Chen
We propose a nested reduced-rank regression (NRRR) approach in fitting regression model with multivariate functional responses and predictors, to achieve tailored dimension reducti…
Pursuing Sources of Heterogeneity in Modeling Clustered Population
Yan Li, Chun Yu, Yize Zhao +3
Researchers often have to deal with heterogeneous population with mixed regression relationships, increasingly so in the era of data explosion. In such problems, when there are man…
Log-Contrast Regression with Functional Compositional Predictors: Linking Preterm Infant's Gut Microbiome Trajectories to Neurobehavioral Outcome
Zhe Sun, Wanli Xu, Xiaomei Cong +2
The neonatal intensive care unit (NICU) experience is known to be one of the most crucial factors that drive preterm infant's neurodevelopmental and health outcomes. It is hypothes…