The Effects of Misspecified Measurement Interval in Multivariate Latent Differential Equation Models

Doctoral Dissertation


Numerous statistical methods exist for the analysis of irregularly spaced time series data (Parzen, 1984). An extension of the Multivariate Latent Differential Equation (MLDE) model (Boker, Neale & Rausch, 2004) for accommodating irregularly-spaced data is provided. Moreover, the current dissertation focuses on how misspecifying an irregular sampling interval as an equal measurement interval effects the estimated model parameters of change of the MLDE model. The accuracy of the estimated model parameters of change was assessed through the use of Monte Carlo simulations. For exponential processes, relative to the MLDE model curvature parameter estimates from equally spaced data, the misspecification of irregularly spaced data has little to no effect on the bias but does decrease the precision of the curvature parameter estimates. For processes exhibiting cyclic change, if a high degree of misspecification exists in the sampling interval, relative to the MLDE model estimates of equally spaced data, the frequency parameter will be underestimated and the damping parameter will be overestimated. Utilizing FIML estimation to correctly define the sampling interval, however, may tend to overestimate the frequency of the process and overestimation of the damping will still exist. The amount of bias incurred in both the frequency and damping parameter due to the incorrect parameterization of time, however, does not appear to be substantial.


Attribute NameValues
  • etd-04182008-143247

Author Stacey S Tiberio
Advisor Cindy Bergeman
Contributor Steven Boker, Committee Member
Contributor Cindy Bergeman, Committee Chair
Contributor Scott Maxwell, Committee Member
Contributor Sy-Miin Chow, Committee Member
Degree Level Doctoral Dissertation
Degree Discipline Psychology
Degree Name PhD
Defense Date
  • 2008-04-11

Submission Date 2008-04-18
  • United States of America

  • Differential Equations

  • Irregularly spaced data

  • Dynamic Models

  • University of Notre Dame

  • English

Record Visibility Public
Content License
  • All rights reserved

Departments and Units


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