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Activity Number: 393 - NLP and Text Analysis
Type: Contributed
Date/Time: Wednesday, August 10, 2022 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #323441
Title: Exploratory Factor Analysis of Data with Incomplete Records
Author(s): Fan Dai* and Karin Dorman and Somak Dutta and Ranjan Maitra
Companies: Michigan Technological University and Iowa State University and Iowa State University and Iowa State University
Keywords: matrix-free computations; partial data; profile likelihood; factor model; L-BFGS-B
Abstract:

Data that are partially observed arise naturally in scientific fields such as astronomy and social science. Traditional methods for analyzing partially recorded data involves incomplete observation deletion or missing value imputation. We develop a CEM (Conditional Expectation Maximization) algorithm under the missing at random (MAR) assumption and further assume a factor covariance structure which can explain the variation in large data set using a few latent factors. We propose a novel matrix-free profile likelihood approach to estimate the covariance parameters with high computational efficiency. Our method is applied to analyze the variability in Gamma Ray Bursts (GRBs), illustrate clustering patterns in hand posture data and reveal differences in grouped semiconductors.


Authors who are presenting talks have a * after their name.

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