Accounting for Nuisance Trends Using Gaussian Processes in Dynamic Structural Equation Modeling
Bibliographic Data
| ID | 21641745 |
|---|---|
| Authors | Reginald Ziedzor (Amplify Education, Inc), Jennifer Koran (0000-0003-2841-4662, Southern Illinois University, corresponding author) |
| Year | 2026 |
| Pages | 1-14 |
| Publication date | 2026-03-05 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Structural Equation Modeling: A Multidisciplinary Journal (JOURNAL) |
| Journal identifiers | ISSN: 1070-5511 • E-ISSN: 1532-8007 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/10705511.2025.2609286 |
| OpenAlex | W7134008862 |
| Language | EN |
| References cited | 26 |
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
Gaussian · Gaussian process · Nuisance · Nuisance parameter · Structural equation modeling · Advanced Multi-Objective Optimization Algorithms · Gaussian Processes and Bayesian Inference · Probabilistic and Robust Engineering Design
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| Highly cited | No |
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