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560 Thu, 8/11/2022, 10:30 AM - 12:20 PM CC-144A
Latent Space Modeling and Dimensionality Reduction — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Jiae Kim, Indiana University
10:35 AM Generalizable Manifold Learning for Dimensional Reduction
Jungeum Kim, Purdue University; Xiao Wang, Purdue University
10:50 AM Inference for Canonical Directions in Canonical Correlation Analysis
Daniel Kessler, University of Michigan; Elizaveta Levina, University of Michigan
11:05 AM Direction Penalized Principal Component Analysis
Youhong Lee, University of California, Santa Barbara; Alex Shkolnik, University of California, Santa Barbara
11:20 AM Projection Expectile Regression for Sufficient Dimension Reduction
Abdul-Nasah Soale, University of Notre Dame
11:35 AM Entrywise Estimation of Singular Vectors of Low-Rank Matrices with Heteroskedasticity and Dependence
Joshua Agterberg, Johns Hopkins University; Zachary Lubberts, Johns Hopkins University; Carey E Priebe, Johns Hopkins University
11:50 AM A Quasi-Likelihood Approach to Latent Space Modeling for Compositional Data
Lun Li, The Ohio State University; Yoonkyung Lee, The Ohio State University
12:05 PM Debiasing Principal Component Score Estimation in Exponential Family PCA for Sparse Count Data
Ruochen Huang, The Ohio State University; Yoonkyung Lee, The Ohio State University