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Activity Number:
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101
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Type:
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Contributed
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Date/Time:
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Monday, August 4, 2008 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #300705 |
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Title:
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Large-Scale Clustering of Dependent Curves
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Author(s):
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Huijing Jiang*+ and Nicoleta Serban
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Companies:
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Georgia Institute of Technology and Georgia Institute of Technology
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Address:
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350448 Georgia Tech Station, Atlanta, GA, 30332,
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Keywords:
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clustering ; spatial dependence ; functional data analysis ; demographics ; Incomplete Choslesky Factorization ; kernel-based decomposition
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Abstract:
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In this paper, we introduce a model-based method for clustering multiple curves or functionals under spatial dependence specified up to a set of unknown parameters. The functionals are decomposed using a semiparametric model where the fixed effects account for the large-scale clustering association and the random effects for the small-scale spatial-dependence variability. The clustering model assumes the clustering membership as a realization from a Markov random field. Within our estimation framework, the emphasis is on a large number of functionals/spatial units with sparsely sampled time points. To overcome the computational cost resulting from large dependence matrix operations, the estimation algorithm includes a two-stage approximation: low-ranked kernel-based decomposition of the dependence matrix and Incomplete Choslesky Factorization of the kernel matrix.
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