Dean Foster is a professor emeritus of statistics at the Wharton School of the University of Pennsylvania. His research interests include machine learning, statistical natural language processing and variable selection. He received his PhD in statistics from the University of Maryland. He is the author of countless papers, including two books, "Business Statistics: A Casebook" and "Business Analysis Using Regression: A Casebook".
Talk: Linear methods for large data
Abstract: Using random matrix theory, we now have some very easy to understand and fast to use methods of computing low rank representations of matrices. I have been using these methods as a hammer to improve several statistical methods. I'll cover two of these in this short talk namely, show how these ideas can be used to speed up regression, and variable selection.