• 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 LibguidePlumX LibguideSubmit an Item

    Statistics

    Display statistics

    Fused Adaptive Lasso for Spatial and Temporal Quantile Function Estimation

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Type
    Article
    Authors
    Sun, Ying cc
    Wang, Huixia J.
    Fuentes, Montserrat
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    Date
    2016-02-22
    Online Publication Date
    2016-02-22
    Print Publication Date
    2016-01-02
    Permanent link to this record
    http://hdl.handle.net/10754/621492
    
    Metadata
    Show full item record
    Abstract
    Quantile functions are important in characterizing the entire probability distribution of a random variable, especially when the tail of a skewed distribution is of interest. This article introduces new quantile function estimators for spatial and temporal data with a fused adaptive Lasso penalty to accommodate the dependence in space and time. This method penalizes the difference among neighboring quantiles, hence it is desirable for applications with features ordered in time or space without replicated observations. The theoretical properties are investigated and the performances of the proposed methods are evaluated by simulations. The proposed method is applied to particulate matter (PM) data from the Community Multiscale Air Quality (CMAQ) model to characterize the upper quantiles, which are crucial for studying spatial association between PM concentrations and adverse human health effects. © 2016 American Statistical Association and the American Society for Quality.
    Citation
    Sun Y, Wang HJ, Fuentes M (2016) Fused Adaptive Lasso for Spatial and Temporal Quantile Function Estimation. Technometrics 58: 127–137. Available: http://dx.doi.org/10.1080/00401706.2015.1017115.
    Sponsors
    The authors thank the valuable suggestions from the associate editor and referees for contributing to the noticeable improvement of the article. This research was partially supported by the US National Science Foundation (NSF) grants DMS-1106862, 1106974, and 1107046, the NSF CAREER award DMS-1149355, and the STATMOS research network on Statistical Methods in Oceanic and Atmospheric Sciences.
    Publisher
    Informa UK Limited
    Journal
    Technometrics
    DOI
    10.1080/00401706.2015.1017115
    ae974a485f413a2113503eed53cd6c53
    10.1080/00401706.2015.1017115
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2021  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    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.