Introducing the FMM Procedure for Finite Mixture Models
Dave Kessler, Allen McDowell · 2012
You’ve collected the data and performed a preliminary analysis with a linear regression. But the residuals have several modes, and transformations don’t help. You need a different approach, and that calls for the FMM procedure. PROC FMM fits finite mixture models, which enable you to describe your data with mixtures of different distributions so you can account for underlying heterogeneity and address overdispersion. PROC FMM offers a wide selection of continuous and discrete distributions, and it provides automated model selection to help you choose the number of components. Bayesian techniques are also available for many analyses. This paper provides an overview of the capabilities of the FMM procedure and illustrates them with applications drawn from a variety of fields.