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Abstract Details
Activity Number:
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313
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics and the Environment
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Abstract - #301387 |
Title:
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Modeling Regular-Clustered Point Patterns: A Generalization of the Neyman-Scott Process
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Author(s):
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Chun Yip Yau*+ and Ji Meng Loh
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Companies:
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Chinese University of Hong Kong and AT&T Labs Research
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Address:
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, , , Hong Kong
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Keywords:
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Neyman-Scott process ;
K-function ;
Gibbs process ;
Regular point process
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Abstract:
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In this paper we introduce a generalization of Neyman-Scott process that allows for regularity in the parent process. In particular, the parent process is a Strauss process, and the offspring process is uniform on a disc centered at each parent. Such a generalization allows for point realizations that show a mix of regularity and clustering in the points. We work out a closed form expression of the K function for this model and use this to fit the model to data. The approach is illustrated by applications to the locations of a species of trees in a rainforest dataset.
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