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

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.

کلیدواژه ها:

Factor Analysis ، Unrestricted contaminated skew normal distribution ، Dimension reduction ، ECM algorithm ، Outliers detection