• Login
    View Item 
    •   TAMU-CC Repository Home
    • TAMU-CC Theses, Dissertations, and Graduate Projects
    • Theses
    • View Item
    •   TAMU-CC Repository Home
    • TAMU-CC Theses, Dissertations, and Graduate Projects
    • Theses
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Testing for Normality in Weak Dependent Time Series

    Thumbnail
    View/Open
    Ji_Hyangim_Thesis.pdf (275.1Kb)
    Date Issued
    2018-12
    2018-12
    Author
    Ji, Hyangim
    Ji, Hyangim
    Metadata
    Show full item record
    URI
    https://hdl.handle.net/1969.6/87827
    https://hdl.handle.net/1969.6/87827
    Abstract
    Many statistical procedures rely on the assumption of normality in the dataset being analyzed. However, many of the standard tests for normality assume independent observations. In this thesis, we look at two methods proposed to test for normality of weakly dependent data, typically time series. The bootstrap-based Anderson-Darling test (BAD) has been proposed as a powerful method to test normality in univariate data, while a technique combining information from skewness, kurtosis, and correlation has been proposed as a method to test multivariate normality in time series. In this thesis, we use simulated data to estimate Type I and Type II error rates for these methods, and compare the results with those of several ”standard” methods that assume independence. We also apply these methods to several real datasets to assess their normality.
    Rights
    This material is made available for use in research, teaching, and private study, pursuant to U.S. Copyright law. The user assumes full responsibility for any use of the materials, including but not limited to, infringement of copyright and publication rights of reproduced materials. Any materials used should be fully credited with its source. All rights are reserved and retained regardless of current or future development or laws that may apply to fair use standards. Permission for publication of this material, in part or in full, must be secured with the author and/or publisher.
    Collections
    • College of Science and Engineering Theses and Dissertations
    • Theses

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    TDL
    Theme by 
    Atmire NV
     

     

    Browse

    All of TAMU-CC RepositoryCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsDepartmentsThis CollectionBy Issue DateAuthorsTitlesSubjectsDepartments

    My Account

    Login

    Statistics

    View Usage Statistics

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    TDL
    Theme by 
    Atmire NV