7 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…
Robust Finite Mixture Regression for Heterogeneous Targets
Jian Liang, Kun Chen, Ming Lin +2
Finite Mixture Regression (FMR) refers to the mixture modeling scheme which learns multiple regression models from the training data set. Each of them is in charge of a subset. FMR…
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…
Statistically Guided Divide-and-Conquer for Sparse Factorization of Large Matrix
Kun Chen, Ruipeng Dong, Wanwan Xu +1
The sparse factorization of a large matrix is fundamental in modern statistical learning. In particular, the sparse singular value decomposition and its variants have been utilized…
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…