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Friday, June 23 • 2:54pm - 3:06pm
L1-penalized Matrix Linear Models for High Throughput Data

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Analysis of high-throughput data can be improved by taking advantage of known relationships between observations. Matrix linear models provide a simple framework for encoding such relationships to enhance detection of associations. Estimation of these models is challenging when the datasets are large and when penalized regression is used. This talk will discuss implementing fast estimation algorithms for L1-penalized matrix linear models as a first-time Julia user and fluent R user. We will share our experiences using Julia as our platform for prototyping, numerical linear algebra, parallel computing, and sharing our method.


Jane Liang

University of Tennessee Health Science Center
Jane Liang recently obtained a bachelor's degree in statistics from UC Berkeley and plans to enter a doctoral program later this year. Currently, she is a scientific programmer working with Dr. Saunak Sen at the University of Tennessee Health Science Center, Department of Preventive... Read More →

Friday June 23, 2017 2:54pm - 3:06pm PDT
West Pauley Pauley Ballroom, Berkeley, CA