192 citations · 340 across the 18 of their papers we have counts for
20 papers
Mining Financial Data using Mixtures of Mirrored Weibull Distributions
Zijun Jia, Sharon X. Lee
Risk management is an important part of financial practice, essential for protecting assets and investments in modern-day volatile markets. This paper proposes a mixture of mirrore…
On mean and/or variance mixtures of normal distributions
Sharon X. Lee, Geoffrey J. McLachlan
Parametric distributions are an important part of statistics. There is now a voluminous literature on different fascinating formulations of flexible distributions. We present a sel…
Multi-Node EM Algorithm for Finite Mixture Models
Sharon X. Lee, Geoffrey J. McLachlan, Kaleb L. Leemaqz
Finite mixture models are powerful tools for modelling and analyzing heterogeneous data. Parameter estimation is typically carried out using maximum likelihood estimation via the E…
Comment on "Hidden truncation hyperbolic distributions, finite mixtures thereof and their application for clustering" Murray, Browne, and \McNicholas
Geoffrey J. McLachlan, Sharon X. Lee
We comment on the paper of Murray, Browne, and McNicholas (2017), who proposed mixtures of skew distributions, which they termed hidden truncation hyperbolic (HTH). They recently m…
On formulations of skew factor models: skew errors versus skew factors
Sharon X. Lee, Geoffrey J. McLachlan
In the past few years, there have been a number of proposals for generalizing the factor analysis (FA) model and its mixture version (known as mixtures of factor analyzers (MFA)) u…
Mixtures of Factor Analyzers with Fundamental Skew Symmetric Distributions
Sharon X. Lee, Tsung-I Lin, Geoffrey J. McLachlan
Mixtures of factor analyzers (MFA) provide a powerful tool for modelling high-dimensional datasets. In recent years, several generalizations of MFA have been developed where the no…
A block EM algorithm for multivariate skew normal and skew t-mixture models
Sharon X Lee, Kaleb L Leemaqz, Geoffrey J McLachlan
Finite mixtures of skew distributions provide a flexible tool for modelling heterogeneous data with asymmetric distributional features. However, parameter estimation via the Expect…
A simple multithreaded implementation of the EM algorithm for mixture models
Sharon X Lee, Kaleb L Lee, Geoffrey J McLachlan
Finite mixture models have been widely used for the modelling and analysis of data from heterogeneous populations. Maximum likelihood estimation of the parameters is typically carr…
Comment on "On Nomenclature, and the Relative Merits of Two Formulations of Skew Distributions" by A. Azzalini, R. Browne, M. Genton, and P. McNicholas
Geoffrey J. McLachlan, Sharon X. Lee
We comment on the recent paper by Azzalini et al. (2015) on two different distributions proposed in the literature for the modelling of data that have asymmetric and possibly long-…
EMMIXcskew: an R Package for the Fitting of a Mixture of Canonical Fundamental Skew t-Distributions
Sharon X. Lee, Geoffrey J. McLachlan
This paper presents an R package EMMIXcskew for the fitting of the canonical fundamental skew t-distribution (CFUST) and finite mixtures of this distribution (FM-CFUST) via maximum…
Supervised Classification of Flow Cytometric Samples via the Joint Clustering and Matching (JCM) Procedure
Sharon X. Lee, Geoffrey J. McLachlan, Saumyadipta Pyne
We consider the use of the Joint Clustering and Matching (JCM) procedure for the supervised classification of a flow cytometric sample with respect to a number of predefined classe…
Finite Mixtures of Canonical Fundamental Skew t-Distributions
Sharon X. Lee, Geoffrey J. McLachlan
This is an extended version of the paper Lee and McLachlan (2014b) with simulations and applications added. This paper introduces a finite mixture of canonical fundamental skew t (…
Comment on "Comparing two formulations of skew distributions with special reference to model-based clustering" by A. Azzalini, R. Browne, M. Genton, and P. McNicholas
Geoffrey J. McLachlan, Sharon X. Lee
In this paper, we comment on the recent comparison in Azzalini et al. (2014) of two different distributions proposed in the literature for the modelling of data that have asymmetri…
Maximum Likelihood Estimation for Finite Mixtures of Canonical Fundamental Skew t-Distributions: the Unification of the Unrestricted and Restricted Skew t-Mixture Models
Sharon X. Lee, Geoffrey J. McLachlan
In this paper, we present an algorithm for the fitting of a location-scale variant of the canonical fundamental skew t (CFUST) distribution, a superclass of the restricted and unre…
The skew-t factor analysis model
Tsung-I Lin, Pal H. Wu, Geoffrey J. McLachlan +1
Factor analysis is a classical data reduction technique that seeks a potentially lower number of unobserved variables that can account for the correlations among the observed varia…
Extending mixtures of factor models using the restricted multivariate skew-normal distribution
Tsung-I Lin, Geoffrey J. McLachlan, Sharon X. Lee
The mixture of factor analyzers (MFA) model provides a powerful tool for analyzing high-dimensional data as it can reduce the number of free parameters through its factor-analytic…
Joint Modeling and Registration of Cell Populations in Cohorts of High-Dimensional Flow Cytometric Data
Saumyadipta Pyne, Kui Wang, Jonathan Irish +10
In systems biomedicine, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multi-variable…
EMMIX-uskew: An R Package for Fitting Mixtures of Multivariate Skew t-distributions via the EM Algorithm
Sharon X. Lee, Geoffrey J. McLachlan
This paper describes an algorithm for fitting finite mixtures of unrestricted Multivariate Skew t (FM-uMST) distributions. The package EMMIX-uskew implements a closed-form expectat…
On Mixtures of Skew Normal and Skew t-Distributions
Sharon X. Lee, Geoffrey J. McLachlan
Finite mixture of skew distributions have emerged as an effective tool in modelling heterogeneous data with asymmetric features. With various proposals appearing rapidly in the rec…
On the fitting of mixtures of multivariate skew t-distributions via the EM algorithm
S. X. Lee, G. J. McLachlan
We show how the expectation-maximization (EM) algorithm can be applied exactly for the fitting of mixtures of general multivariate skew t (MST) distributions, eliminating the need…