Abstract
Multivariate time-dependent data arise when multiple features are measured for a set of units over time instants. When dealing with such data, flexible statistical tools are needed in order to account for characteristics such as the relations among both time observations and variables, the possible subject heterogeneity and arbitrarily shaped time evolutions. In this talk we present a new co-clustering strategy, which groups simultaneously variables and individuals, and is adequate both for longitudinal and functional data. The approach proposed relies on the Shape Invariant Model which is embedded in the Latent Block Model, representing the most popular model-based co-clustering strategy. To account for the specific features of the Shape Invariant Model, the estimation procedure is carried out by means of a suitable modification of the SEM-Gibbs algorithm. The resulting methodology flexibly introduces different, and possibly user-defined, notions of cluster and, by partitioning matrices into homogeneous blocks, provides parsimonious representations of high-dimensional and complex structured time-dependent data. Lastly, the explicit modelling of time evolution allows for meaningful interpretations of the clusters.