Statistics and Data Science Seminar
Haiying Wang
University of New Hampshire
Leveraging Algorithms for Logistic Regression with Massive Data
Abstract: For massive data with super-large sample size n, it is computationally infeasible
to obtain maximum likelihood estimates for unknown parameters, especially when the
estimator does not have a close-form solution. This paper proposes fast leveraging
algorithms to efficiently approximate the maximum likelihood estimates of unknown
parameters in logistic regression models with binary responses, one of the most commonly
used models in practice for classification. We theoretically prove the consistency
of the leveraging algorithms, develop nearly optimal two-step leveraging strategies, and
evaluate the performance of the proposed methods using synthetic and real data sets.
Wednesday February 18, 2015 at 4:00 PM in SEO 636