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Activity Number:
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69
- Network Analysis
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Type:
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Contributed
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Date/Time:
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Monday, August 3, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #313762
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Title:
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Network Structure Inference from Grouped Data
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Author(s):
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Yunpeng Zhao* and Peter Bickel and Charles Weko
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Companies:
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Arizona State Univ and University of California, Berkeley and U.S. Army
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Keywords:
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Network estimation; Identifiability; Bernoulli mixture model
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Abstract:
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Statistical network analysis typically deals with inference concerning various parameters of an observed network. In several applications, especially those from social sciences, behavioral information concerning groups of subjects are observed. In such data sets, even though a network structure is present it is not typically observed. These are referred to as implicit networks. We describe a model-based framework to uncover the implicit network structure and address related inferential questions. Theoretical properties such as model idenfiability and estimation consistency will be discussed.
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Authors who are presenting talks have a * after their name.
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