Accounting for Nuisance Trends Using Gaussian Processes in Dynamic Structural Equation Modeling

In modeling intensive longitudinal data with the dynamic structural equation modeling framework, this demonstration analyzes the impact of Gaussian process priors in the presence or absence of a significant trend component on the parameter estimates obtained through Bayesian analysis. The results show that even when not the focus of a research study, failing to account for the presence of trend may severely impact statistical inference and Gaussian processes present a viable option for accounting for them. Also, in the absence of trends, Gaussian processes do not impact the quality of parameter estimates when introduced in an over-specified model.