A robust factor analysis model based on the unrestricted contaminated skew-normal with missing information

Hashemi, Farzane

doi:10.22103/jmmr.2025.25228.1798

A robust factor analysis model based on the unrestricted contaminated skew-normal with missing information

Document Type : Research Paper

Author

Farzane Hashemi

Department of Statistics, University of Kashan, Kashan, Iran

10.22103/jmmr.2025.25228.1798

Abstract

The rise of advanced data collection technologies has led to a surge in the use of factor analysis (FA) for dimensionality reduction. With more measurements comes a greater risk of outliers, potentially resulting in biased estimates, decreased stability and robustness, and ultimately inaccurate conclusions. To accommodate asymmetric high-dimension data and outliers, we developed a FA model using the unrestricted contaminated skew normal distributions as the latent factors that automatically detects outliers. Maximum likelihood parameter estimates are computed using EM-type algorithms that we developed. We use an information-based method to calculate the asymptotic standard errors of the parameters. The asymptotic properties of the maximum likelihood estimators and the model's outlier robustness were examined via numerous simulation studies. Pima data serve as case studies to further showcase the model's practical use in social data analysis.

Keywords

Factor analysis

Unrestricted contaminated skew normal distribution

Dimension reduction

ECM algorithm

Outliers detection

Subjects

Statistics and Probability

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