13 citations · 13 across the 2 of their papers we have counts for
4 papers
BayesSUR: An R package for high-dimensional multivariate Bayesian variable and covariance selection in linear regression
Zhi Zhao, Marco Banterle, Leonardo Bottolo +3
In molecular biology, advances in high-throughput technologies have made it possible to study complex multivariate phenotypes and their simultaneous associations with high-dimensio…
Bayesian Variable Selection for Gaussian copula regression models
Angelos Alexopoulos, Leonardo Bottolo
We develop a novel Bayesian method to select important predictors in regression models with multiple responses of diverse types. A sparse Gaussian copula regression model is used t…
A global-local approach for detecting hotspots in multiple-response regression
Hélène Ruffieux, Anthony C. Davison, Jörg Hager +4
We tackle modelling and inference for variable selection in regression problems with many predictors and many responses. We focus on detecting hotspots, i.e., predictors associated…
Evolutionary Stochastic Search for Bayesian model exploration
Leonardo Bottolo, Sylvia Richardson
Implementing Bayesian variable selection for linear Gaussian regression models for analysing high dimensional data sets is of current interest in many fields. In order to make such…