Abstract Number:
3308
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Mihoko Minami (1), Cleridy Lennert-Cody (2)
Institutions:
(1) Keio University, Yokohama, Japan, (2) Inter-American Tropical Tuna Commission, La Jolla, California
Co-Author:
First Author:
Presenting Author:
Abstract Text:
Scientists often collect samples on characteristics of different observation units and wonder whether the characteristics of the observation units have similar distributional structure. In this study, we propose a new Bayesian clustering method for distributions that uses a T-EP (Truncated Ewens-Pitman) distribution as the prior for the partitioning parameters, that is, for the number of clusters and the cluster sizes. For a given number of clusters, we consider an entropy-based objective function that is naturally derived from the modified Jensen-Shannon divergence between two distributions. This leads to a hierarchical Bayesian clustering method for distributions.
As a motivational example, we introduce yellowfin tuna fork length data collected from the tuna catch of purse-seine vessels that operated in the eastern Pacific Ocean. The hierarchical Bayesian clustering method, applied to density estimates of yellowfin tuna fork length for 5-degree square areas, was used to explore spatial structure in the length composition of the tuna catch.
Keywords:
Hierarchical Bayesian model|Modified Jensen-Shannon divergence|Clustering for distributions|Truncated Ewens-Pitman distribution|Density estimates|
Sponsors:
Section on Statistics and the Environment
Tracks:
Spatio-temporal statistics
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