Title: Recent advances in sparse Bayesian factor analysis
Authors: Sylvia Fruehwirth-Schnatter - WU Vienna University of Economics and Business (Austria) [presenting]
Abstract: Factor analysis is a popular method to obtain a sparse representation of the covariance matrix of multivariate observations. We review some recent research in the area of sparse Bayesian factor analysis that tries to achieve additional sparsity in a factor model through the use of point mass mixture priors. Identifiability issues that arise from introducing zeros in the factor loading matrix are discussed in detail. Common prior choices in Bayesian factor analysis are reviewed and MCMC estimation is briefly outlined. Applications to a small-sized psychological data set as well as a financial applications to exchange rate data and stock returns serve as an illustration.