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Activity Number: 494 - Advanced Developments in Methods and Algorithms for Modern Complex Imaging Data
Type: Invited
Date/Time: Thursday, August 11, 2022 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract #320328
Title: Mixture of Multivariate Sparse Regressions Modeling for Oceanographic Flow Cytometry Data
Author(s): Jacob Bien* and Sangwon Hyun and Fran├žois Ribalet and Mattias Cape
Companies: University of Southern California and University of Southern California and University of Washington and University of Washington
Keywords: flow cytometry; lasso; mixture of regressions; alternating direction method of multipliers; oceanography; variable selection

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.

Authors who are presenting talks have a * after their name.

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