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

Zita Oravecz (), Julie Wood () and Nilam Ram ()
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Zita Oravecz: The Pennsylvania State University
Julie Wood: The Pennsylvania State University
Nilam Ram: The Pennsylvania State University

Chapter Chapter 3 in Continuous Time Modeling in the Behavioral and Related Sciences, 2018, pp 55-78 from Springer

Abstract: 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.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-77219-6_3

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DOI: 10.1007/978-3-319-77219-6_3

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