TL;DR
This work develops a generalized-distribution-based Bayesian approach for consistent network-guided variable selection in high-dimensional linear regression and incorporates hierarchical spike-and-slab priors on the regression coefficients and the elements of the inverse covariance matrix.
High-dimensional non-normal longitudinal data are ubiquitous across fields such as genomics, biomedicine, microbiome research, and the social sciences. Such data often combine non-normal responses, within-subject dependence and sparse population effects. We develop a structured variational Bayesian procedure: variation...
Multidimensional array data, or tensors, arise naturally in neuroimaging and other high-dimensional applications. We propose a parsimonious Bayesian tensor regression model for studies in which a brain image is the response and predictors are vector-valued covariates. The method extends Bayesian envelope dimension redu...
High-dimensional, multimodal predictors and partially observed multivariate outcomes are common in psychometric research. However, existing regularization methods often do not accommodate hierarchical predictor structures and are primarily designed for univariate outcomes. We propose SHIM, a Bayesian framework for stru...
It is suggested that copula-based feature representations combined with deep learning provide a flexible approach for heterogeneous treatment effect estimation, particularly in high-dimensional settings with complex covariate dependence.
Network analysis for multivariate time series is popular in many fields, from neuroscience to seismology. The inverse spectral density is a common choice for time series network analysis due to its representation of the frequency domain correlation between two variables after removing the best linear predictor of all o...
This article proposes an innovative framework, INLA-MBN, that integrates multilevel BNs (MBNs) with the integrated nested Laplace approximation (INLA), thereby facilitating efficient structure and parameter learning in both longitudinal and cross-sectional multilevel data contexts.
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