Citation

Dai F, Dutta S, Maitra R. Journal of Computational and Graphical Statistics 2020; 29(3): 675-680.

Copyright

(Copyright © 2020)

DOI

10.1080/10618600.2019.1704296

PMID

unavailable

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. © 2020 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.

Language: en

Keywords

fMRI; EM algorithm; L-BFGS-B; Lanczos algorithm; Profile likelihood