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Activity Number:
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9
- When Location Is Random: Advances in Statistical Modeling and Inference for Spatial Point Processes
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Type:
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Invited
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Date/Time:
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Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #322214
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Title:
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Nonparametric Inference for Point Processes
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Author(s):
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Tailen Hsing* and Jinqi Shen
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Companies:
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University of Michigan and University of Michigan
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Keywords:
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Multivariate point process ;
nonparametric estimation
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Abstract:
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Data that describe the occurrences of events of interest in terms of location/time are prevalent in environmental research. In this paper we consider novel nonparametric approaches for statistical models of point process data. Particular attention will be given to the inference of dependence in the context of multivariate point processes. Theoretical as well computational aspects of our approaches will be investigated.
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Authors who are presenting talks have a * after their name.
