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Jaeyong Lee (Seoul National University)

11 December 2014 @ 11:00

 

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Date:
11 December 2014
Time:
11:00
Event Category:

Dependent species sampling models

We consider a novel Bayesian nonparametric model for density estimation with an underlying spatial structure. The model is built on a class of species sampling models, which are discrete random probability measures that can be represented as a mixture of random support points and random weights. Specifically, we construct a collection of spatially dependent species sampling models and propose a mixture model based on this collection. The key idea is the introduction of spatial dependence by modeling the weights through a conditional autoregressive model (Sun et al.; 1999). We present an extensive simulation study to compare the performance of the proposed model with competitors based on a Dirichlet process without dependence. The proposed model compares favorably to these alternatives. We apply the method to the estimation of summer pre- cipitation density functions using Climate Prediction Center Merged Analysis of Precipitation (CMAP) data from a 33-year period over the Korean peninsula.