EMMIXcskew: an R package for the fitting of a mixture of canonical fundamental skew t-distributions
Files
(Published Version)
Date
2018
Authors
Lee, S.X.
McLachlan, G.J.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
Journal of Statistical Software, 2018; 83(3)
Statement of Responsibility
Sharon X. Lee, Geoffrey J. McLachlan
Conference Name
Abstract
This paper presents the R package EMMIXcskew for the fitting of the canonical fundamental skew t-distribution (CFUST) and finite mixtures of CFUST distributions (FMCFUST) via maximum likelihood (ML). The CFUST distribution provides a flexible family to model non-normal data, with parameters for capturing skewness and heavy-tails in the data. It formally encompasses the normal, t, and skew normal distributions as special and/or limiting cases. A few other versions of the skew t-distributions are also nested within the CFUST distribution. In this paper, an expectation-maximization (EM) algorithm is described for computing the ML estimates of the parameters of the FM-CFUST model, and different strategies for initializing the algorithm are discussed and illustrated. The methodology is implemented in the EMMIXcskew package, and examples are presented using two real datasets. The EMMIXcskew package contains functions to fit the FM-CFUST model, including procedures for generating different initial values. Additional features include random sample generation and contour visualization in 2D and 3D.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
JSS is committed to electronic open-access publishing since its foundation in 1996 and has chosen to apply the Creative Commons Attribution License (CCAL) to all articles. Under the CCAL, authors retain ownership of the copyright for their article, but authors allow anyone to download, reuse, reprint, modify, distribute, and/or copy articles in JSS, so long as the original authors and source are credited. This broad license was developed to facilitate open access to, and free use of, original works of all types. Applying this standard license to your work will ensure your right to make your work freely and openly available. This work is licensed under the licenses Paper: Creative Commons Attribution 3.0 Unported License Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.