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Activity Number:
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80
- Sufficient Dimension Reduction and Applications
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Type:
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Topic-Contributed
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Date/Time:
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Monday, August 9, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #317558
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Title:
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Least Squares and Maximum Likelihood Estimation of Sufficient Reductions in Regressions with Matrix Valued Predictors
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Author(s):
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Efstathia Bura* and Daniel Kapla and Ruth Pfeiffer
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Companies:
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TU Wien and TU Wien and NCI-DCEG
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Keywords:
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Abstract:
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We propose methods to estimate sufficient reductions of matrix-valued predictors for regression or classification. We assume that the first moment of the predictor matrix given the response can be decomposed into a row and column component via a Kronecker product structure. We obtain least squares and maximum likelihood estimates of the sufficient reductions of the matrix predictors, derive statistical properties of the resulting estimates and present fast computational algorithms with assured convergence. The performance of the proposed approaches in regression and classification is compared in simulations. We illustrate the methods on two examples, using longitudinally measured serum biomarker and neuroimaging data.
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Authors who are presenting talks have a * after their name.
