General location model with factor analyzer covariance matrix structure and its applications

Leila Amiri (), Mojtaba Khazaei () and Mojtaba Ganjali ()
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Leila Amiri: Shahid Beheshti University
Mojtaba Khazaei: Shahid Beheshti University
Mojtaba Ganjali: Shahid Beheshti University

Advances in Data Analysis and Classification, 2017, vol. 11, issue 3, No 8, 593-609

Abstract: Abstract General location model (GLOM) is a well-known model for analyzing mixed data. In GLOM one decomposes the joint distribution of variables into conditional distribution of continuous variables given categorical outcomes and marginal distribution of categorical variables. The first version of GLOM assumes that the covariance matrices of continuous multivariate distributions across cells, which are obtained by different combination of categorical variables, are equal. In this paper, the GLOMs are considered in both cases of equality and unequality of these covariance matrices. Three covariance structures are used across cells: the same factor analyzer, factor analyzer with unequal specific variances matrices (in the general and parsimonious forms) and factor analyzers with common factor loadings. These structures are used for both modeling covariance structure and for reducing the number of parameters. The maximum likelihood estimates of parameters are computed via the EM algorithm. As an application for these models, we investigate the classification of continuous variables within cells. Based on these models, the classification is done for usual as well as for high dimensional data sets. Finally, for showing the applicability of the proposed models for classification, results from analyzing three real data sets are presented.

Keywords: General location model; Factor analyzer; Common factor loadings; Classification; The EM algorithm; 62H25; 62H30 (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11634-016-0258-6

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