# Cluster weighted models with multivariate skewed distributions for functional data

arXiv: https://arxiv.org/abs/2504.12683 · HTML: https://mlsanity.com/paper/2504.12683/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 17 Apr 2025.

## Builds on (most cited first, 9 of 9)

- [A Mixture of Generalized Hyperbolic Distributions](https://mlsanity.com/paper/1305.1036/) (2013)
- [Robust Clustering in Regression Analysis via the Contaminated Gaussian Cluster-Weighted Model](https://mlsanity.com/paper/1409.6019/) (2014)
- [Outlier detection in multivariate functional data through a contaminated mixture model](https://mlsanity.com/paper/2106.07222/) (2021)
- [Functional regression clustering with multiple functional gene expressions](https://mlsanity.com/paper/2112.00224/) (2021)
- [Unsupervised learning of regression mixture models with unknown number of components](https://mlsanity.com/paper/1409.6981/) (2014)
- [Multivariate response and parsimony for Gaussian cluster-weighted models](https://mlsanity.com/paper/1411.0560/) (2014)
- [Multivariate Cluster Weighted Models Using Skewed Distributions](https://mlsanity.com/paper/2103.09792/) (2021)
- [Matrix Normal Cluster-Weighted Models](https://mlsanity.com/paper/2104.11900/) (2021)
- [Cluster weighted models for functional data](https://mlsanity.com/paper/2503.05159/) (2025)

## Abstract (arXiv)

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.