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

  • Zita Oravecz
  • Julie Wood
  • Nilam Ram
Chapter

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.

Notes

Acknowledgment

The research reported in this paper was sponsored by grant #48192 from the John Templeton Foundation, the National Institutes of Health (R01 HD076994, UL TR000127), and the Penn State Social Science Research Institute.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.The Pennsylvania State UniversityState CollegeUSA

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