Journal or Book Title
Journal of Computational and Graphical Statistics
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.
American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America
Dai, Fan; Dutta, Somak; and Maitra, Ranjan, "A Matrix-Free Likelihood Method for Exploratory Factor Analysis of High-Dimensional Gaussian Data" (2020). Statistics Publications. 303.