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Activity Number: 87 - Invited ePoster Session: a Statistical Smörgåsbord
Type: Invited
Date/Time: Sunday, July 29, 2018 : 8:30 PM to 10:30 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #329506
Title: Mixtures of Contaminated Shifted Asymmetric Laplace Factor Analyzers
Author(s): Brian C Franczak*
Companies: MacEwan University
Keywords: Cluster analysis; Finite mixture models; Shifted Asymmetric Laplace; EM Algorithm; Classification

Cluster analysis can be lucidly defined as the process of sorting similar objects into groups. When a finite mixture model is utilized for cluster analysis, we call the process model-based clustering. This presentation will discuss the development of a mixture of contaminated shifted asymmetric Laplace factor analyzers (MCSALFA). This model will be well suited for the analysis of high-dimensional data; specifically, where the number of variables exceeds the number of observations. In addition to providing a classification of similar observations, the MCSALFA will also provide a classification of an observation as being either `good' or `bad', unifying the fields of model-based clustering and outlier detection. From a methodological standpoint, the MCSALFA will unify the factor analysis model and the contaminated mixture model and it will require the development of a robust parameter estimation scheme, which will be based on a variant of the expectation-maximization (EM) algorithm. The implementation of this algorithm in R will be discussed and the classification performance of the MCSALFAs will be demonstrated using a real data set.

Authors who are presenting talks have a * after their name.

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