# Mixtures of Generalized Hyperbolic Distributions and Mixtures of Skew-t Distributions for Model-Based Clustering with Incomplete Data

arXiv: https://arxiv.org/abs/1703.02177 · HTML: https://mlsanity.com/paper/1703.02177/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2017.

## Built on by (most cited first, 4 of 4)

- [A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Them](https://mlsanity.com/paper/2305.09536/) (2023)
- [A Robust and Flexible EM Algorithm for Mixtures of Elliptical Distributions with Missing Data](https://mlsanity.com/paper/2201.12020/) (2022)
- [Fast model-based clustering of partial records](https://mlsanity.com/paper/2103.16336/) (2021)
- [Kernel-Based Anomaly Detection Using Generalized Hyperbolic Processes](https://mlsanity.com/paper/2501.15265/) (2025)

## Abstract (arXiv)

Robust clustering from incomplete data is an important topic because, in many practical situations, real data sets are heavy-tailed, asymmetric, and/or have arbitrary patterns of missing observations. Flexible methods and algorithms for model-based clustering are presented via mixture of the generalized hyperbolic distributions and its limiting case, the mixture of multivariate skew-t distributions. An analytically feasible EM algorithm is formulated for parameter estimation and imputation of missing values for mixture models employing missing at random mechanisms. The proposed methodologies are investigated through a simulation study with varying proportions of synthetic missing values and illustrated using a real dataset. Comparisons are made with those obtained from the traditional mixture of generalized hyperbolic distribution counterparts by filling in the missing data using the mean imputation method.