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