Online Program Home
My Program

Abstract Details

Activity Number: 359
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract #318691
Title: Clustering Multiscale Spatial Functional Data with Application to Precipitation Regimes Identification
Author(s): Haozhe Zhang* and Zhengyuan Zhu and Shuiqing Yin
Companies: Iowa State University and Iowa State University and Beijing Normal University
Keywords: Cluster ; spatial dependence ; multiscale ; uncertainty assessment ; meteorology
Abstract:

The identification of precipitation regimes is important for many purposes such as agricultural planning, water resource management, and return period estimation. Since precipitation and other related meteorological data typically exhibit spatial dependency and different characteristics at different time scales, clustering such data present unique challenges. In this paper, we develop a flexible model-based approach to cluster multiscale spatial functional data to address such problems. The underlying clustering model is a functional linear model with within-curve dependence, and the cluster memberships are assumed to be a realization from a Markov random field with geographic covariates. Based on diagnosis plots and the asymptotic distribution of posterior probabilities of cluster memberships, we provide a method for assessing the final cluster assignments of curves. The methodology is applied to a precipitation data from China to identify precipitation regions, which is shown to be better than the conventional approach. Though the focus of this study is on precipitation data, the clustering method is generally applicable to other environmental data with similar structure.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

 
 
Copyright © American Statistical Association