Composite likelihood inference for simultaneous clustering and dimensionality reduction of mixed-type longitudinal data

Relatore:Monia Ranalli - Università Roma La Sapienza

  • Data: 27 aprile 2023 dalle 16:00 alle 17:00

  • Luogo: da definire

We introduce a multivariate hidden Markov model (HMM) for mixed-type (continuous and ordinal) variables. Since we assume that some of the considered variables may not contribute to the clustering structure, a hidden Markov-based model is built such that discriminative and noise dimensions can be recognized. The variables are considered to be linear combinations of two independent sets of latent factors where one contains the information about the cluster structure, following an HMM, and the other one contains noise dimensions distributed as a multivariate normal (and it does not change over time). The resulting model is parsimonious, but its computational burden may be cumbersome. To overcome any computational issue, a composite likelihood approach is introduced to estimate model parameters. The choice of the model, in terms of optimal numbers of latent states and discriminant factors, is based on a double cross validation procedure. The proposal is evaluated through a simulation study and an application to real data.

Laura Anderlucci