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On Fitting a Continuous-Time Stochastic Process Model in the Bayesian Framework

In: Continuous Time Modeling in the Behavioral and Related Sciences

Author

Listed:
  • Zita Oravecz

    (The Pennsylvania State University)

  • Julie Wood

    (The Pennsylvania State University)

  • Nilam Ram

    (The Pennsylvania State University)

Abstract

Process models can be viewed as mathematical tools that allow researchers to formulate and test theories on the data-generating mechanism underlying observed data. In this chapter we highlight the advantages of this approach by proposing a multilevel, continuous-time stochastic process model to capture the dynamical homeostatic process that underlies observed intensive longitudinal data. Within the multilevel framework, we also link the dynamical processes parameters to time-varying and time-invariant covariates. However, estimating all model parameters (e.g., process model parameters and regression coefficients) simultaneously requires custom-made implementation of the parameter estimation; therefore we advocate the use of a Bayesian statistical framework for fitting these complex process models. We illustrate application to data on self-reported affective states collected in an ecological momentary assessment setting.

Suggested Citation

  • Zita Oravecz & Julie Wood & Nilam Ram, 2018. "On Fitting a Continuous-Time Stochastic Process Model in the Bayesian Framework," Springer Books, in: Kees van Montfort & Johan H. L. Oud & Manuel C. Voelkle (ed.), Continuous Time Modeling in the Behavioral and Related Sciences, chapter 0, pages 55-78, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-77219-6_3
    DOI: 10.1007/978-3-319-77219-6_3
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