ORCID Profile
0000-0002-6391-135X
Current Organisations
University of Adelaide
,
University of Queensland
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Publisher: Springer Science and Business Media LLC
Date: 02-09-2020
Publisher: University of Queensland Library
Publisher: IEEE
Date: 12-2012
Publisher: Foundation for Open Access Statistic
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 29-07-2019
Publisher: IEEE
Date: 08-2017
Publisher: SAGE Publications
Date: 04-09-2017
Abstract: This article introduces a robust extension of the mixture of factor analysis models based on the restricted multivariate skew- t distribution, called mixtures of skew- t factor analysis (MSTFA) model. This model can be viewed as a powerful tool for model-based clustering of high-dimensional data where observations in each cluster exhibit non-normal features such as heavy-tailed noises and extreme skewness. Missing values may be frequently present due to the incomplete collection of data. A computationally feasible EM-type algorithm is developed to carry out maximum likelihood estimation and create single imputation of possible missing values under a missing at random mechanism. The numbers of factors and mixture components are determined via penalized likelihood criteria. The utility of our proposed methodology is illustrated through analysing both simulated and real datasets. Numerical results are shown to perform favourably compared to existing approaches.
Publisher: Springer Science and Business Media LLC
Date: 28-02-2015
Publisher: Springer Singapore
Date: 2019
Publisher: Wiley
Date: 22-10-2015
DOI: 10.1002/CYTO.A.22789
Abstract: We present an algorithm for modeling flow cytometry data in the presence of large inter-s le variation. Large-scale cytometry datasets often exhibit some within-class variation due to technical effects such as instrumental differences and variations in data acquisition, as well as subtle biological heterogeneity within the class of s les. Failure to account for such variations in the model may lead to inaccurate matching of populations across a batch of s les and poor performance in classification of unlabeled s les. In this paper, we describe the Joint Clustering and Matching (JCM) procedure for simultaneous segmentation and alignment of cell populations across multiple s les. Under the JCM framework, a multivariate mixture distribution is used to model the distribution of the expressions of a fixed set of markers for each cell in a s le such that the components in the mixture model may correspond to the various populations of cells, which have similar expressions of markers (that is, clusters), in the composition of the s le. For each class of s les, an overall class template is formed by the adoption of random-effects terms to model the inter-s le variation within a class. The construction of a parametric template for each class allows for direct quantification of the differences between the template and each s le, and also between each pair of s les, both within or between classes. The classification of a new unclassified s le is then undertaken by assigning the unclassified s le to the class that minimizes the distance between its fitted mixture density and each class density as provided by the class templates. For illustration, we use a symmetric form of the Kullback-Leibler ergence as a distance measure between two densities, but other distance measures can also be applied. We show and demonstrate on four real datasets how the JCM procedure can be used to carry out the tasks of automated clustering and alignment of cell populations, and supervised classification of s les.
Publisher: Springer Science and Business Media LLC
Date: 21-05-2013
Publisher: Springer India
Date: 2016
Publisher: Springer Science and Business Media LLC
Date: 20-10-2012
Publisher: Elsevier BV
Date: 09-2016
Publisher: Elsevier BV
Date: 2021
Publisher: IEEE
Date: 11-2016
Publisher: Elsevier BV
Date: 12-2016
Publisher: Informa UK Limited
Date: 24-08-2019
Publisher: Public Library of Science (PLoS)
Date: 07-2014
Publisher: Springer Singapore
Date: 2017
Publisher: Springer Science and Business Media LLC
Date: 31-07-2013
Publisher: Springer Science and Business Media LLC
Date: 07-12-2014
Publisher: Springer International Publishing
Date: 2019
Publisher: Wiley
Date: 06-02-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2018
Publisher: Wiley
Date: 05-03-2019
DOI: 10.1002/CPE.5208
Publisher: Springer International Publishing
Date: 2016
Publisher: Elsevier
Date: 2017
Publisher: Springer International Publishing
Date: 2017
Publisher: IEEE
Date: 12-2010
Publisher: Annual Reviews
Date: 07-03-2019
DOI: 10.1146/ANNUREV-STATISTICS-031017-100325
Abstract: The important role of finite mixture models in the statistical analysis of data is underscored by the ever-increasing rate at which articles on mixture applications appear in the statistical and general scientific literature. The aim of this article is to provide an up-to-date account of the theory and methodological developments underlying the applications of finite mixture models. Because of their flexibility, mixture models are being increasingly exploited as a convenient, semiparametric way in which to model unknown distributional shapes. This is in addition to their obvious applications where there is group-structure in the data or where the aim is to explore the data for such structure, as in a cluster analysis. It has now been three decades since the publication of the monograph by McLachlan & Basford (1988) with an emphasis on the potential usefulness of mixture models for inference and clustering. Since then, mixture models have attracted the interest of many researchers and have found many new and interesting fields of application. Thus, the literature on mixture models has expanded enormously, and as a consequence, the bibliography here can only provide selected coverage.
Publisher: Foundation for Open Access Statistic
Date: 2013
Publisher: Elsevier BV
Date: 2016
Publisher: Springer Science and Business Media LLC
Date: 22-10-2013
Start Date: 2016
End Date: 2018
Funder: Australian Research Council
View Funded ActivityStart Date: 2018
End Date: 2020
Funder: Australian Research Council
View Funded Activity