Presenter: Dr. Fan Dai
Title: A Matrix-Free Likelihood Method for Exploratory Factor Analysis of High-Dimensional Gaussian Data
Abstract: We propose 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.
Bio: Fan Dai obtained a B.S. in Statistics from Shanghai University of Finance and Economics in 2013 and an M.S. in Statistical and Economic Modeling from Duke University in 2015. Fan is currently a PhD candidate in Statistics at Iowa State University and works with Professor Somak Dutta and Ranjan Maitra. She is preparing to graduate in summer 2020 and will join the Department of Mathematical Sciences at Michigan Technological University as an assistant professor in Fall 2020. Her research interests include high dimensional data inference, directional data, spatial statistics and statistical consulting.
After the presentation, there will be a short time for discussion and questions afterwards.