Online Program Home
  My Program

Abstract Details

Activity Number: 52 - New Challenges in Complex Data Analysis
Type: Invited
Date/Time: Sunday, July 30, 2017 : 4:00 PM to 5:50 PM
Sponsor: Korean International Statistical Society
Abstract #321987
Title: Leveraging Algorithms for Super-Large Sample Logistic Regression
Author(s): Ping Ma*
Companies: University of Georgia
Keywords: MLE ; Leverage ; asymptotics
Abstract:

For massive data with super-large sample size, it is computationally infeasible to obtain maximum likelihood estimates for unknown parameters, especially when the estimators do not have closed-form solutions. In this talk, I will present fast leveraging algorithms to efficiently approximate the maximum likelihood estimates in logistic regression models with binary responses, one of the most commonly used models in practice for classification. I will also present some theoretical results on consistency and asymptotic normality of the estimators. Synthetic and real data sets are used to evaluate the practical performance of the proposed methods.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2017 program

 
 
Copyright © American Statistical Association