Cristina Anton
MacEwan University, Edmonton, Canada

We propose a method for clustering multivariate functional data with outliers based on a family of latent mixture of $t$-distributions models. The parameters of these models are estimated using an expectation maximization algorithm. The proposed method is illustrated for simulated data and for the analysis of the traffic flow in Edmonton, Canada.