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Activity Number:
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136
- Recent Advances in Dimension Reduction
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Type:
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Contributed
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Date/Time:
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Monday, July 29, 2019 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #306728
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Title:
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Matrix-Free Likelihood Methods for Exploratory Factor Analysis with 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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Profile likelihood;
Partial SVD;
Lanczos algorithm;
L-BFGS-B;
fMRI;
Suicidal ideation data
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Abstract:
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This paper proposes a novel profile likelihood method for estimating the covariance parameters in exploratory factor analysis (EFA) with high-dimensional Gaussian data. By implementing a Lanczos algorithm and a limited-memory quasi-Newton method, we develop a matrix free algorithm (HDFA) which does partial singular value decomposition (partial SVD) for data matrix where number of observations n is typically less than the dimension p and it only requires limited amount of memory during likelihood maximization. We perform simulation study with both the randomly generated models and the data-driven models. Results indicate that HDFA substantially outperforms the EM algorithm in all cases. Furthermore, Our algorithm is applied to fit factor models for a fMRI dataset with suicidal attempters, suicidal nonattempters and a control group.
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Authors who are presenting talks have a * after their name.
