Model-Based Clustering and Classification Using Mixtures of Multivariate Skewed Power Exponential Distributions

Utkarsh J. Dang (), Michael P.B. Gallaugher, Ryan P. Browne and Paul D. McNicholas
Additional contact information
Utkarsh J. Dang: Carleton University
Michael P.B. Gallaugher: Baylor University
Ryan P. Browne: University of Waterloo
Paul D. McNicholas: McMaster University

Journal of Classification, 2023, vol. 40, issue 1, No 7, 145-167

Abstract: Abstract Families of mixtures of multivariate power exponential (MPE) distributions have already been introduced and shown to be competitive for cluster analysis in comparison to other mixtures of elliptical distributions, including mixtures of Gaussian distributions. A family of mixtures of multivariate skewed power exponential distributions is proposed that combines the flexibility of the MPE distribution with the ability to model skewness. These mixtures are more robust to variations from normality and can account for skewness, varying tail weight, and peakedness of data. A generalized expectation-maximization approach, which combines minorization-maximization and optimization based on accelerated line search algorithms on the Stiefel manifold, is used for parameter estimation. These mixtures are implemented both in the unsupervised and semi-supervised classification frameworks. Both simulated and real data are used for illustration and comparison to other mixture families.

Keywords: Generalized expectation-maximization algorithm; Mixture models; Model-based classification; Model-based clustering; Multivariate skewed power exponential distribution (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00357-022-09427-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jclass:v:40:y:2023:i:1:d:10.1007_s00357-022-09427-7

Ordering information: This journal article can be ordered from
http://www.springer. ... hods/journal/357/PS2

DOI: 10.1007/s00357-022-09427-7

Access Statistics for this article

Journal of Classification is currently edited by Douglas Steinley

More articles in Journal of Classification from Springer, The Classification Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().