@article{ref1,
title="A Matrix-Free Likelihood Method for Exploratory Factor Analysis of High-Dimensional Gaussian Data",
journal="Journal of Computational and Graphical Statistics",
year="2020",
author="Dai, F. and Dutta, S. and Maitra, R.",
volume="29",
number="3",
pages="675-680",
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.<p /><p>Language: en</p>",
language="en",
issn="1061-8600",
doi="10.1080/10618600.2019.1704296",
url="http://dx.doi.org/10.1080/10618600.2019.1704296"
}