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Activity Number: 414 - Models for Environmental Processes
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #304213 Presentation
Title: Spatial Cluster Detection with Threshold Quantile Regression
Author(s): Junho Lee* and Ying Sun and Huixia Judy Wang
Companies: King Abdullah University of Science and Technology and King Abdullah University of Science and Technology and The George Washington University
Keywords: Quantile regression; Spatial cluster detection; Spatial threshold effect; Threshold model; Threshold quantile regression

Identifying clusters of spatial units with common regression coefficients is useful for discerning the distinctive relationship between a response and covariates relative to the background. In this paper, we develop a spatial cluster detection approach using a threshold quantile regression model. We introduce two threshold variables in the quantile regression model to define a spatial cluster. The proposed test statistic for identifying the spatial cluster is the supremum of the Wald process over the space of threshold parameters. We establish the limiting distribution of the test statistic under the null hypothesis that the quantile regression coefficient is the same over the entire spatial domain at the given quantile level. The performance of our proposed method is assessed by simulation studies. The proposed method is also applied to analyze the particulate matter (PM2.5) and ozone (O3) concentrations data in the U.S. to identify spatial regions where O3 has a distinctive effect on PM2.5 at high quantile levels.

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

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