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Activity Number:
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494
- Advanced Developments in Methods and Algorithms for Modern Complex Imaging Data
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Type:
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Invited
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Date/Time:
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Thursday, August 11, 2022 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Computing
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Abstract #320328
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Title:
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Mixture of Multivariate Sparse Regressions Modeling for Oceanographic Flow Cytometry Data
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Author(s):
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Jacob Bien* and Sangwon Hyun and François Ribalet and Mattias Cape
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Companies:
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University of Southern California and University of Southern California and University of Washington and University of Washington
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Keywords:
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flow cytometry;
lasso;
mixture of regressions;
alternating direction method of multipliers;
oceanography;
variable selection
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Abstract:
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Although microscopic, phytoplankton in the ocean are extremely important to all of life and are together responsible for as much photosynthesis as all plants on land combined. Today, oceanographers are able to collect flow cytometry data in real time while onboard a moving ship, providing them with fine-scale information about the distribution of phytoplankton across thousands of kilometers. We present a novel mixture of multivariate sparse regressions model to estimate the time-varying phytoplankton subpopulations while simultaneously identifying the specific environmental covariates that are predictive of the observed changes to these subpopulations.
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Authors who are presenting talks have a * after their name.
