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Activity Number:
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560
- Latent Space Modeling and Dimensionality Reduction
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Type:
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Contributed
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Date/Time:
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Thursday, August 11, 2022 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #323220
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Title:
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Inference for Canonical Directions in Canonical Correlation Analysis
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Author(s):
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Daniel Kessler* and Elizaveta Levina
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Companies:
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University of Michigan and University of Michigan
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Keywords:
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Canonical Correlation Analysis;
CCA;
Brain Imaging
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Abstract:
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Canonical Correlation Analysis (CCA) is a method for analyzing pairs of random vectors; it learns a sequence of paired linear transformations such that the resultant canonical variates are maximally correlated within pairs while uncorrelated across pairs. The parameters estimated by CCA include both the “canonical correlations” as well as the “canonical directions'' which characterize the transformations. CCA has seen a resurgence of popularity with applications including brain imaging and genomics where the goal is often to identify relationships between high-dimensional -omics data with more moderately sized behavioral or phenotypic measurements. Inference in CCA applications is typically limited to testing whether the canonical correlations are nonzero. Inference for the canonical directions has received relatively little attention in the statistical literature and in practice the directions are interpreted descriptively. We discuss several approaches for conducting inference on canonical directions obtained by CCA. We conduct thorough simulation studies to assess inferential validity in various settings and apply the methods to a brain imaging data set.
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Authors who are presenting talks have a * after their name.
