A Computationally More Efficient Bayesian Approach for Estimating Continuous-Time Models. Issue 6 (1st November 2020)

• Record Type: Journal Article
• Authors: Hecht, Martin; Zitzmann, Steffen
• Is Part Of: Structural equation modeling. Volume 27:Issue 6(2020)
• Page Start: 829
• Page End: 840
• Publication Date: 2020-11-01
• Subjects: Continuous-time modeling -- Bayesian analysis -- run time optimization -- intensive longitudinal data -- structural equation modeling; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2020.1719107
• Languages: English
• ISSNs: 1070-5511

Abstract

ABSTRACT: Continuous-time modeling is gaining in popularity as more and more intensive longitudinal data need to be analyzed. Current Bayesian software implementations of continuous-time models suffer from rather high, inadequate run times. Therefore, we apply a model reformulation approach to reduce run time. In a simulation study, we investigate the estimation quality and run time gain. We then illustrate our optimized Bayesian continuous-time model estimation and compare it to established continuous-time modeling software using an empirical example. Parameter estimates and inference statistics were very comparable, while run times were very different. Our approach reduces the run times for Bayesian estimations of continuous-time models from hours to minutes.