We provide a survey of recent results on covariance estimation for heavy-tailed distributions.
By unifying ideas scattered in the literature, we propose user-friendly methods that facilitate …

This paper considers the problem of high-dimensional sparse precision matrix estimation
under Laplacian constraints. We prove that the Laplacian constraints bring favorable …

We introduce a k-banded Cholesky prior for estimating high-dimensional bandable
precision matrices using a modified Cholesky decomposition. The bandable assumption is …

The conventional Quadratic Discriminant Analysis (QDA) encounters a significant hurdle
due to parameter scaling complexities on the order of O (p2), rendering it impractical for the …

This paper studies the estimation of large precision matrices and Cholesky factors obtained
by observing a Gaussian process at many locations. Under general assumptions on the …

As large-scale neural recordings become common, many neuroscientific investigations are
focused on identifying functional connectivity from spatio-temporal measurements in two or …

Recently, there has been a surge of interest in hypothesis testing methods for combining
dependent studies without explicitly assessing their dependence. Among these, the Cauchy …

The modified Cholesky decomposition (MCD) method is commonly used in precision matrix
estimation assuming that the random variables have a specified order. In this paper, we …

Scalable Bayesian High-dimensional Local Dependence Learning Page 1 Bayesian
Analysis (2023) 18, Number 1, pp. 25–47 Scalable Bayesian High-dimensional Local …

In many scientific areas, data with mixed quantitative and qualitative (QQ) responses are
commonly encountered with a large number of predictors. By exploring the association …