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Activity Number: 101 - Foundation for Big Data Analysis
Type: Invited
Date/Time: Monday, July 31, 2017 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract #321898 View Presentation
Title: Inference for Big Data
Author(s): Larry Wasserman*
Companies: Carnegie Mellon
Keywords: big data ; exchangeability

In principle, very large datasets make inference easy since we can assume we are in the asymptotic regime. But large datasets can be heterogeneous which can cause substantial bias. We consider identifying and correcting for heterogeneity.

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

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