ucgs_a_1704296_sm7141.zip (3.13 MB)
A Matrix-Free Likelihood Method for Exploratory Factor Analysis of High-Dimensional Gaussian Data
Version 3 2021-09-29, 16:19
Version 2 2020-02-07, 16:52
Version 1 2019-12-19, 03:40
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posted on 2021-09-29, 16:19 authored by Fan Dai, Somak Dutta, Ranjan MaitraThis 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.