Sylvia Frühwirth-Schnatter | Department of Finance, Accounting and Statistics, Vienna University of Economics and Business

Factor analysis is a popular method to obtain a sparse representation of the covariance matrix of multivariate observations. The present talk reviews 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. Special attention is given to be question how to select the number of factors. Applications to psychological data set as well as an applications to financial returns serve as illustration.


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