A0854
Title: Heterogeneous g-priors for networks with dyadic covariance structures
Authors: Rigers Behluli - University of Warwick - Ca\' Foscari University (Italy) [presenting]
Roberto Casarin - University Ca' Foscari of Venice (Italy)
Mark Steel - University of Warwick (United Kingdom)
Abstract: The increasing availability of multivariate data calls for the use of matrix variate models for identifying hidden patterns within the data and for predicting the variables of interest. The aim is to propose a novel regression model for sequences of matrix data with dyadic covariance structure and develop a Bayesian procedure for model selection. It is shown that it is possible to characterize the dyadic variance-covariance matrix analytically and perform inference and model selection using mixtures of g-priors adapted to accommodate the heterogeneity induced by dyadic covariance. Some theoretical results are presented, and the algorithms used to sample low- and high-dimensional model spaces. Simulation results and a real data comparison to an established method for dyadic covariance estimation are presented.
