Cluster weighted models using skewed distributions for functional data

Music City Center 

We propose a new model-based clustering method, funWeightClustSkew, for heterogeneous functional linear regression data. This method is based on the functional high dimensional data clustering (funHDDC) method. We use multivariate functional principal component analysis, and we assume that the scores have one of three skewed distributions: the skew-t, the variance-gamma or the normal-inverse Gaussian distributions. We consider several parsimonious models, and we propose a variant of the Expectation-Maximization (EM) algorithm for parameter estimation.