Probabilistic Disjoint Principal Component Analysis. (2nd January 2019)

• Record Type: Journal Article
• Authors: Ferrara, Carla; Martella, Francesca; Vichi, Maurizio
• Is Part Of: Multivariate behavioral research. Volume 54:Number 1(2019)
• Page Start: 47
• Page End: 61
• Publication Date: 2019-01-02
• Subjects: Probabilistic model -- partition of variables -- maximum-likelihood estimation; Psychometrics -- Periodicals; Psychology, Experimental -- Periodicals; Psychology, Experimental; Psychometrics; Periodicals; 150.15195
• DOI: 10.1080/00273171.2018.1485006
• Languages: English
• ISSNs: 0027-3171

Abstract

Abstract: One of the most relevant problems in principal component analysis and factor analysis is the interpretation of the components/factors. In this paper, disjoint principal component analysis model is extended in a maximum-likelihood framework to allow for inference on the model parameters. A coordinate ascent algorithm is proposed to estimate the model parameters. The performance of the methodology is evaluated on simulated and real data sets.