Cluster weighted models with multivariate skewed distributions for functional data
This work addresses clustering challenges in functional data analysis, which is incremental as it builds on existing methods by incorporating skewed distributions.
The authors tackled the problem of clustering functional data by proposing a method that extends cluster weighted models to incorporate multivariate skewed distributions, achieving improved clustering performance as demonstrated on simulated data and the Air Quality dataset.
We propose a clustering method, funWeightClustSkew, based on mixtures of functional linear regression models and three skewed multivariate distributions: the variance-gamma distribution, the skew-t distribution, and the normal-inverse Gaussian distribution. Our approach follows the framework of the functional high dimensional data clustering (funHDDC) method, and we extend to functional data the cluster weighted models based on skewed distributions used for finite dimensional multivariate data. We consider several parsimonious models, and to estimate the parameters we construct an expectation maximization (EM) algorithm. We illustrate the performance of funWeightClustSkew for simulated data and for the Air Quality dataset.