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Activity Number: 453 - Novel Theory and Methods in Big Data Analytics
Type: Invited
Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #326626 Presentation
Title: Statistical Inference for Big Data via Optimal Subsampling
Author(s): HaiYing Wang*
Companies: University of Connecticut
Keywords: Logistic Regression; Massive Data; Optimal subsampling; Poisson sampling; Rare Events Data

With limited computing resources compared with the exponentially growing data volume, subsampling-based methods have demonstrated pervasive potential in making better use of a fixed amount of computing power. In this talk, focusing on logistic regression, I will discus the problem of statistical inference for the Optimal Subsampling Method under the A-optimality Criterion (OSMAC). For OSMAC, the subsampling probabilities depend on unknown parameters, so a two-step adaptive algorithm is used. Consistency and asymptotic normality of the estimators from the two-step adaptive algorithm are established, and the asymptotic variance-covariance matrices for different estimators are compared. Extensive numerical results are also provided to demonstrate practical performance of the estimators based on OSMAC.

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

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