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    Uncertainty of a detected spatial cluster in 1D: quantification and visualization

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    JunhoLee_Aug2017_Stat.pdf
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    Type
    Article
    Authors
    Lee, Junho
    Gangnon, Ronald E.
    Zhu, Jun
    Liang, Jingjing
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2017-10-19
    Online Publication Date
    2017-10-19
    Print Publication Date
    2017
    Permanent link to this record
    http://hdl.handle.net/10754/625937
    
    Metadata
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    Abstract
    Spatial cluster detection is an important problem in a variety of scientific disciplines such as environmental sciences, epidemiology and sociology. However, there appears to be very limited statistical methodology for quantifying the uncertainty of a detected cluster. In this paper, we develop a new method for the quantification and visualization of uncertainty associated with a detected cluster. Our approach is defining a confidence set for the true cluster and visualizing the confidence set, based on the maximum likelihood, in time or in one-dimensional space. We evaluate the pivotal property of the statistic used to construct the confidence set and the coverage rate for the true cluster via empirical distributions. For illustration, our methodology is applied to both simulated data and an Alaska boreal forest dataset. Copyright © 2017 John Wiley & Sons, Ltd.
    Citation
    Lee J, Gangnon RE, Zhu J, Liang J (2017) Uncertainty of a detected spatial cluster in 1D: quantification and visualization. Stat. Available: http://dx.doi.org/10.1002/sta4.161.
    Sponsors
    Funding has been provided by a USDA Cooperative State Research, Education and Extension Service (CSREES) McIntire-Stennis project. We thank the School of Natural Resources and Agricultural Sciences, University of Alaska Fairbanks, for the development and maintenance of the Cooperative Alaska Forest Inventory, and thank the Global Forest Biodiversity Initiative (GFBI) for establishing data-sharing protocols and standards for international collaborative research projects. We also thank Thomas Malone for his insights on Alaska boreal forest.
    Publisher
    Wiley
    Journal
    Stat
    DOI
    10.1002/sta4.161
    Additional Links
    http://onlinelibrary.wiley.com/doi/10.1002/sta4.161/full
    ae974a485f413a2113503eed53cd6c53
    10.1002/sta4.161
    Scopus Count
    Collections
    Articles; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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