KUKU

KU ScholarWorks

  • myKU
  • Email
  • Enroll & Pay
  • KU Directory
    • Login
    View Item 
    •   KU ScholarWorks
    • Dissertations and Theses
    • Dissertations
    • View Item
    •   KU ScholarWorks
    • Dissertations and Theses
    • Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Bayesian Estimation of a Continuous-Time Model for Discretely-Observed Panel Data

    Thumbnail
    View/Open
    Boulton_ku_0099D_13547_DATA_1.pdf (924.6Kb)
    Issue Date
    2014-08-31
    Author
    Boulton, Aaron Jacob
    Publisher
    University of Kansas
    Format
    192 pages
    Type
    Dissertation
    Degree Level
    Ph.D.
    Discipline
    Psychology
    Rights
    This item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author.
    Metadata
    Show full item record
    Abstract
    Continuous-time models are used in many areas of science. However, in psychology and related fields, continuous-time models are often difficult to apply because only a small number of repeated observations are typically available. One promising model that has been suggested for such data is the Exact Discrete Model (EDM)—a set of mathematical relations that connect the discrete-time autoregressive cross-lagged (ARCL) panel model to an underlying continuous-time model. To date, several frequentist approaches have been developed for estimating the underlying continuous-time model parameters via the EDM. On the contrary, Bayesian approaches have not yet been explored. Therefore, the purpose of this project was to outline a Bayesian implementation of the EDM with non-informative priors and compare its performance to two frequentist approaches—EDM-SEM (Oud & Jansen, 2000) and Oversampling (Singer, 2012}—under proper model specification and variable experimental conditions. Data were generated under different combinations of sample size, number of time points, and population parameter values for a bivariate panel model. In addition, starting values for the frequentist methods were set to data generating values or randomly perturbed. Results from the three estimation approaches were equivalent at moderate and large sample sizes. The Bayesian implementation resulted in fewer non-converged and improper solutions compared to the frequentist approaches in nearly all experimental conditions. Parameter estimates were slightly less biased and less variable under frequentist estimation at small sample sizes. The Bayesian approach and Oversampling generally provided equivalent or better interval coverage compared to the EDM-SEM procedure across all conditions. Finally, model fit statistics calculated under the Bayesian approach via posterior predictive modeling checking were less sensitive to sample size than those calculated for the frequentist methods; however, proposed cutoff values did not correspond to Type I error rates. To summarize, preliminary support for a non-informative Bayesian implementation of the EDM was found. In addition, Oversampling appears to be a promising method for frequentist estimation of the EDM. Alternative prior specifications, modeling extensions, and the performance of these approaches under less ideal analytic conditions are important areas for further study.
    URI
    http://hdl.handle.net/1808/16843
    Collections
    • Dissertations [4454]
    • Psychology Dissertations and Theses [459]

    Items in KU ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.


    We want to hear from you! Please share your stories about how Open Access to this item benefits YOU.


    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

    Browse

    All of KU ScholarWorksCommunities & CollectionsThis Collection

    My Account

    Login

    Statistics

    View Usage Statistics

    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

    The University of Kansas
      Contact KU ScholarWorks
    Lawrence, KS | Maps
     
    • Academics
    • Admission
    • Alumni
    • Athletics
    • Campuses
    • Giving
    • Jobs

    The University of Kansas prohibits discrimination on the basis of race, color, ethnicity, religion, sex, national origin, age, ancestry, disability, status as a veteran, sexual orientation, marital status, parental status, gender identity, gender expression and genetic information in the University’s programs and activities. The following person has been designated to handle inquiries regarding the non-discrimination policies: Director of the Office of Institutional Opportunity and Access, IOA@ku.edu, 1246 W. Campus Road, Room 153A, Lawrence, KS, 66045, (785)864-6414, 711 TTY.

     Contact KU
    Lawrence, KS | Maps