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From Regular OLS to High-Dimensional Linear Regression by Annie Tang, Williams College

Wed, September 29th, 2021
1:10 pm
- 1:50 pm

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From Regular OLS to High-Dimensional Linear Regression by Annie Tang, Williams College

From Regular OLS to High-Dimensional Linear Regression by Annie Tang, Williams College, Statistics Colloquium, Wednesday, September 29, 1:10 – 1:50 pm, North Science Building 017, Wachenheim

Abstract:  Big data, known for its recent role in the growth of deep learning, has also given rise to a separate class of problems, where the number of predictors exceed the number of samples. I will first introduce how high-dimensional data is usually defined in statistics and machine learning. Then, I will demonstrate issues that arise when we move from “regular” data to the high-dimensional realm in linear regression and common methods that adapt the OLS solution to high-dimensional data. Finally, I will discuss the idea and benefits of a Bayesian solution in this context, and briefly, my specific research area, called empirical Bayes.

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