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Clustering spatial functional data using a geographically weighted Dirichlet process.
- Source :
-
Canadian Journal of Statistics . Sep2024, Vol. 52 Issue 3, p696-712. 17p. - Publication Year :
- 2024
-
Abstract
- We propose a Bayesian nonparametric clustering approach to study the spatial heterogeneity effect for functional data observed at spatially correlated locations. We consider a geographically weighted Chinese restaurant process equipped with a conditional autoregressive prior to capture fully the spatial correlation of function curves. To sample efficiently from our model, we customize a prior called Quadratic Gamma, which ensures conjugacy. We design a Markov chain Monte Carlo algorithm to infer simultaneously the posterior distributions of the number of groups and the grouping configurations. The superior numerical performance of the proposed method over competing methods is demonstrated using simulated examples and a U.S. annual precipitation study. [ABSTRACT FROM AUTHOR]
- Subjects :
- *STATISTICAL correlation
*HETEROGENEITY
Subjects
Details
- Language :
- English
- ISSN :
- 03195724
- Volume :
- 52
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Canadian Journal of Statistics
- Publication Type :
- Academic Journal
- Accession number :
- 178994796
- Full Text :
- https://doi.org/10.1002/cjs.11803