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arXiv · 1907.11970

A Matrix--free Likelihood Method for Exploratory Factor Analysis of High-dimensional Gaussian Data

Fan Dai·Somak Dutta·Ranjan Maitra
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Abstract

This paper 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.

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Fan Dai, Somak Dutta, Ranjan Maitra. 2019-07-27. A Matrix--free Likelihood Method for Exploratory Factor Analysis of High-dimensional Gaussian Data. https://doi.org/10.1080/10618600.2019.1704296

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