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CC = Walter E. Washington Convention Center M = Marriott Marquis Washington, DC
* = applied session ! = JSM meeting theme
Activity Details
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560
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Thu, 8/11/2022,
10:30 AM -
12:20 PM
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CC-144A
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Latent Space Modeling and Dimensionality Reduction — Contributed Papers
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Section on Statistical Learning and Data Science
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Chair(s): Jiae Kim, Indiana University
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10:35 AM
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Generalizable Manifold Learning for Dimensional Reduction
Jungeum Kim, Purdue University; Xiao Wang, Purdue University
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10:50 AM
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Inference for Canonical Directions in Canonical Correlation Analysis
Daniel Kessler, University of Michigan; Elizaveta Levina, University of Michigan
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11:05 AM
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Direction Penalized Principal Component Analysis
Youhong Lee, University of California, Santa Barbara; Alex Shkolnik, University of California, Santa Barbara
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11:20 AM
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Projection Expectile Regression for Sufficient Dimension Reduction
Abdul-Nasah Soale, University of Notre Dame
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11:35 AM
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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
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11:50 AM
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A Quasi-Likelihood Approach to Latent Space Modeling for Compositional Data
Lun Li, The Ohio State University; Yoonkyung Lee, The Ohio State University
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12:05 PM
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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
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