Using estimation of demand for the George Washington/Jefferson NationalForest as a case study, it is shown that in a stratified/clustered on-sitesample, latent heterogeneity needs to be accounted for twice: first to accountfor dispersion in the data caused by unobservability of the process that...

We describe a class of sparse latent factor models, called graphical factor models (GFMs), and relevant sparse learning algorithms for posterior mode estimation. Linear, Gaussian GFMs have sparse, orthogonal factor loadings matrices, that, in addition to sparsity of the implied covariance...

Summary of Banco de España Working Paper no. 2105