• Login
    View Item 
    •   Home
    • Office of Sponsored Research (OSR)
    • KAUST Funded Research
    • Publications Acknowledging KAUST Support
    • View Item
    •   Home
    • Office of Sponsored Research (OSR)
    • KAUST Funded Research
    • Publications Acknowledging KAUST Support
    • 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

    Single-Index Additive Vector Autoregressive Time Series Models

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Type
    Article
    Authors
    LI, YEHUA
    Genton, Marc G. cc
    KAUST Grant Number
    KUS-C1-016-04
    Date
    2009-09
    Permanent link to this record
    http://hdl.handle.net/10754/599636
    
    Metadata
    Show full item record
    Abstract
    We study a new class of nonlinear autoregressive models for vector time series, where the current vector depends on single-indexes defined on the past lags and the effects of different lags have an additive form. A sufficient condition is provided for stationarity of such models. We also study estimation of the proposed model using P-splines, hypothesis testing, asymptotics, selection of the order of the autoregression and of the smoothing parameters and nonlinear forecasting. We perform simulation experiments to evaluate our model in various settings. We illustrate our methodology on a climate data set and show that our model provides more accurate yearly forecasts of the El Niño phenomenon, the unusual warming of water in the Pacific Ocean. © 2009 Board of the Foundation of the Scandinavian Journal of Statistics.
    Citation
    LI Y, GENTON MG (2009) Single-Index Additive Vector Autoregressive Time Series Models. Scandinavian Journal of Statistics 36: 369–388. Available: http://dx.doi.org/10.1111/j.1467-9469.2009.00641.x.
    Sponsors
    Genton's research was supported in part by a National Science Foundation CMG grant ATM-0620624 and by Award no. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). The authors thank the editor, the associate editor and two referees for constructive suggestions that have improved the content and presentation of this article. The authors also thank Salil Mahajan and Ramalingam Saravanan from the Department of Atmospheric Sciences at Texas A&M University for providing the climate data set.
    Publisher
    Wiley
    Journal
    Scandinavian Journal of Statistics
    DOI
    10.1111/j.1467-9469.2009.00641.x
    ae974a485f413a2113503eed53cd6c53
    10.1111/j.1467-9469.2009.00641.x
    Scopus Count
    Collections
    Publications Acknowledging KAUST Support

    entitlement

     
    DSpace software copyright © 2002-2023  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.