Work

Reducing subspace models for large‐scale covariance regression

Year: 2021

Type: article

Abstract: We develop an envelope model for joint mean and covariance regression in the large p, small n setting. In contrast to existing envelope methods, which improve mean estimates by incorporating estimates of the covariance structure, we focus on identifying covariance heterogeneity by incorporating information about mean-level differences. We use a Monte Carlo EM algorithm to identify a low-dimensional subspace that explains differences in both means and covariances as a function of covariates, and then use MCMC to estimate the posterior uncertainty conditional on the inferred low-dimensional subspace. We demonstrate the utility of our model on a motivating application on the metabolomics of aging. We also provide R code that can be used to develop and test other generalizations of the response envelope model.

Source: Biometrics

DOI: 10.1111/biom.13531

Author: Alexander M. Franks

Institution: University of California, Santa Barbara

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Institutes of Health, National Cancer Institute

Awards: Multi-group covariance estimation for metabolomic analysis with applications to neurodegenerative disease, G8395683368