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The Information Systems and Statistics Research Seminar Series
Presented by the Paul H. Chook Department of Information Systems and Statistics
“A fair comparison of estimators in high-dimensions”
Arian Maleki, Assistant Professor, Columbia University
Tuesday, November. 14, 12:30pm-1:45pm, NVC 11-175, IS Lab
From: Prof. Rongning Wu, Paul H. Chook Department of Information Systems and Statistics
In the past twenty years the field of statistics has faced relatively new types of datasets in which both the number of observations, n, and the number of predictors, p, are very large. The standard theoretical frameworks for studying such datasets, such as the rate-optimal minimaxity platform, are often incapable of providing a fair comparison among different estimators. We will also show that sometimes the results that are obtained from such platforms have led to misleading conclusions. The goal of this talk is to show that all the problems raised above can be addressed through a sharp high-dimensional asymptotic analysis of estimators. To demonstrate our point we focus on two classical problems in statistics, i.e. variable selection and linear regression and provide a sharp comparison among some of the most popular algorithms for each problem.