• Login
    View Item 
    •   Home
    • Research
    • Articles
    • View Item
    •   Home
    • Research
    • Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguideTheses and Dissertations LibguideSubmit an Item

    Statistics

    Display statistics

    Spatial cluster detection with threshold quantile regression

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    env-21-0054-Final.pdf
    Size:
    2.881Mb
    Format:
    PDF
    Description:
    Accepted manuscript
    Embargo End Date:
    2022-07-13
    Download
    Thumbnail
    Name:
    env-21-0054-Final-SupportingInformation.pdf
    Size:
    270.1Kb
    Format:
    PDF
    Description:
    supporting Information
    Embargo End Date:
    2022-07-13
    Download
    Type
    Article
    Authors
    Lee, Junho
    Sun, Ying cc
    Judy Wang, Huixia
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Environmental Statistics Group
    Statistics Program
    KAUST Grant Number
    OSR-2019-CRG7-3800
    Date
    2021-07-13
    Embargo End Date
    2022-07-13
    Submitted Date
    2020-07-03
    Permanent link to this record
    http://hdl.handle.net/10754/670235
    
    Metadata
    Show full item record
    Abstract
    Spatial cluster detection, which is the identification of spatial units adjacent in space associated with distinctive patterns of data of interest relative to background variation, is useful for discerning spatial heterogeneity in regression coefficients. Some real studies with regression-based models on air quality data show that there exists not only spatial heterogeneity but also heteroscedasticity between air pollution and its predictors. Since the low air quality is a well-known risk factor for mortality, various cardiopulmonary diseases, and preterm birth, the analysis at the tail would be of more interest than the center of air pollution distribution. In this article, we develop a spatial cluster detection approach using a threshold quantile regression model to capture the spatial heterogeneity and heteroscedasticity. 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 (PM 2.5 ) concentration and aerosol optical depth (AOD) data in the Northeastern United States in order to study geographical heterogeneity in the association between AOD and PM 2.5 at different quantile levels.
    Citation
    Lee, J., Sun, Y., & Judy Wang, H. (2021). Spatial cluster detection with threshold quantile regression. Environmetrics. doi:10.1002/env.2696
    Sponsors
    This publication is based upon work supported by King Abdullah University of Science and Technology (KAUST), Officeof Sponsored Research (OSR) under Award No. OSR-2019-CRG7-3800; by the IR/D program and grant DMS-1712760from the National Science Foundation. Any opinion, findings, and conclusions or recommendations expressed in thismaterial are those of the authors and do not necessarily reflect the views of the National Science Foundation. The authorsthank Dr. Howard H. Chang for providing the satellite data.
    Publisher
    Wiley
    Journal
    Environmetrics
    DOI
    10.1002/env.2696
    Additional Links
    https://onlinelibrary.wiley.com/doi/10.1002/env.2696
    ae974a485f413a2113503eed53cd6c53
    10.1002/env.2696
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2022  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.