Activity Number:
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83
- Frontiers in Analysis of Microbiome Data: From Methods to Applications
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Type:
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Invited
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Date/Time:
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Monday, August 8, 2022 : 8:30 AM to 10:20 AM
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Sponsor:
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ENAR
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Abstract #319188
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Title:
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Tensor Reduced-Rank Regression with Incomplete Observations, with Application to Longitudinal Microbiome Analysis
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Author(s):
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Gen Li*
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Companies:
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University of Michigan
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Keywords:
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tensor array;
reduced-rank regression;
microbiome;
log-contrast model;
ADMM
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Abstract:
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Multivariate longitudinal data measured on a regular grid can be concisely represented as a three-way tensor array (i.e., sample-by-feature-by-time). Missing observations are commonly encountered in such data since not all samples are measured at every time point. The missing data impose significant challenges for statistical analysis. This work develops a novel scalar-on-tensor regression framework, called TRIO, which effectively leverages all available observations in a design tensor for accurate parameter estimation and prediction. We propose a parsimonious model for the design tensor and regression coefficient matrix and devise a computationally efficient algorithm to estimate model parameters with flexible regularization. Numerical studies demonstrate the superior performance of the proposed method over competitors. The method is further applied to a preterm infant study to predict neurodevelopment response from longitudinal microbiome data.
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Authors who are presenting talks have a * after their name.
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