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Activity Number:
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444
- Modern and Practical Solutions to Difficult High-Dimensional Regression Problems
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Type:
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Invited
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Date/Time:
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Wednesday, July 31, 2019 : 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 #300181
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Title:
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Computationally-Efficient High-Dimensional Interaction Modeling
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Author(s):
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Guo Yu and Ryan Tibshirani and Jacob Bien*
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Companies:
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University of Washington and Carnegie Mellon University and University of Southern California
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Keywords:
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sparse;
lasso;
interactions;
variable selection;
regression;
screening
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Abstract:
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Data sets with 100,000 or more predictor variables are common in fields such as biology. Yet if we wish to fit regression models with interactions, we are faced with enormous variable selection problems with at least five billion features. The scale of such problems demands seeking out computationally cheap methods (both in time and memory) that still have sound statistical properties. Motivated by these large-scale problem sizes, we develop a new method that performs feature selection for regression models with interactions. We provide theoretical results indicating favorable statistical properties and empirical results showing our method applied to large-scale interaction problems with strong statistical performance.
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Authors who are presenting talks have a * after their name.
