# Reducing subspace models for large-scale covariance regression.

@article{Franks2021ReducingSM, title={Reducing subspace models for large-scale covariance regression.}, author={Alexander M. Franks}, journal={Biometrics}, year={2021} }

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 which explains differences in both means and covariances as a function of covariates… Expand

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