A Matrix-Free Likelihood Method for Exploratory Factor Analysis of High-Dimensional Gaussian Data. Issue 3 (2nd July 2020)

• Record Type: Journal Article
• Authors: Dai, Fan; Dutta, Somak; Maitra, Ranjan
• Is Part Of: Journal of computational and graphical statistics. Volume 29:Issue 3(2020)
• Page Start: 675
• Page End: 680
• Publication Date: 2020-07-02
• Subjects: EM algorithm -- fMRI -- Lanczos algorithm -- L-BFGS-B -- Profile likelihood; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2019.1704296
• Languages: English
• ISSNs: 1061-8600

Abstract

ABSTRACT: This technical note proposes a novel profile likelihood method for estimating the covariance parameters in exploratory factor analysis of high-dimensional Gaussian datasets with fewer observations than number of variables. An implicitly restarted Lanczos algorithm and a limited-memory quasi-Newton method are implemented to develop a matrix-free framework for likelihood maximization. Simulation results show that our method is substantially faster than the expectation-maximization solution without sacrificing accuracy. Our method is applied to fit factor models on data from suicide attempters, suicide ideators, and a control group. Supplementary materials for this article are available online.