Campus Units

Statistics

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

2-7-2020

Journal or Book Title

Journal of Computational and Graphical Statistics

DOI

10.1080/10618600.2019.1704296

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.

Comments

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Computational and Graphical Statistics on February 7, 2020, available online: 10.1080/10618600.2019.1704296. Posted with permission.

Copyright Owner

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

Language

en

File Format

application/pdf

Available for download on Sunday, February 07, 2021

Published Version

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