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Activity Number:
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405
- Student Paper Award and Chambers Statistical Software Award
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 5, 2020 : 1:00 PM to 2:50 PM
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Sponsor:
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Section on Statistical Computing
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Abstract #309654
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Title:
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A Matrix-Free Method for Exploratory Factor Analysis of High-Dimensional Gaussian Data
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Author(s):
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Fan Dai* and Somak Dutta and Ranjan Maitra
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Companies:
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Iowa State University and Iowa State University and Iowa State University
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Keywords:
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fMRI;
Implicitly restarted Lanczos algorithm;
L-BFGS-B;
Profile likelihood
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Abstract:
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This paper proposes a novel profile likelihood method for estimating the covariance parameters in the 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 our method to be substantially faster than the expectation-maximization solution without sacrificing accuracy. Our method is applied to data from suicide attempters, suicide ideators and a control group.
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Authors who are presenting talks have a * after their name.
