Academic Journal
Bayesian Inference of Dynamic Mediation Models for Longitudinal Data
| Τίτλος: | Bayesian Inference of Dynamic Mediation Models for Longitudinal Data |
|---|---|
| Συγγραφείς: | Shuai Zhao, Zhiyong Zhang, Hong Zhang |
| Πηγή: | Structural Equation Modeling: A Multidisciplinary Journal. 31:14-26 |
| Στοιχεία εκδότη: | Informa UK Limited, 2023. |
| Έτος έκδοσης: | 2023 |
| Θεματικοί όροι: | Statistics and Probability, Artificial intelligence, Methods for Causal Inference in Observational Studies, Path analysis (statistics), Bayesian inference, Social Sciences, Experimental and Cognitive Psychology, Bayesian probability, 01 natural sciences, FOS: Economics and business, Methods for Handling Missing Data in Statistical Analysis, Inference, Sociology, 0504 sociology, Machine learning, FOS: Mathematics, Network Analysis of Psychopathology and Mental Disorders, Psychology, Econometrics, 0101 mathematics, Data mining, Mediation Analysis, Emotion Dynamics, 05 social sciences, Mixed-Effects Models, Psychometric Models, Mediation, Social science, Computer science, FOS: Sociology, FOS: Psychology, Longitudinal Data Analysis, Physical Sciences, Dynamic Bayesian network, Mathematics, Causal inference |
| Περιγραφή: | Mediation analysis is widely applied in various fields of science, such as psychology, epidemiology, and sociology. In practice, many psychological and behavioral phenomena are dynamic, and the corresponding mediation effects are expected to change over time. However, most existing mediation methods assume a static mediation effect over time, which overlooks the dynamic nature of mediation effect. To address this issue, we propose dynamic mediation models that can capture the dynamic nature of the mediation effect. Specifically, we model the path parameters of mediation models as auto-regressive (AR) processes of time that can vary over time. Additionally, we define the mediation effect under the potential outcome framework, and examine its identification and causal interpretation. Bayesian methods utilizing Gibbs sampling are adopted to estimate unknown parameters in the proposed dynamic mediation models. We further evaluate our proposed models and methods through extensive simulations and illustrate their application through a real data application. |
| Τύπος εγγράφου: | Article Other literature type |
| Γλώσσα: | English |
| ISSN: | 1532-8007 1070-5511 |
| DOI: | 10.1080/10705511.2023.2230519 |
| DOI: | 10.6084/m9.figshare.23802421 |
| DOI: | 10.6084/m9.figshare.23802421.v1 |
| DOI: | 10.60692/1ggbr-mmb77 |
| DOI: | 10.60692/fgdjz-vt454 |
| Rights: | CC BY |
| Αριθμός Καταχώρησης: | edsair.doi.dedup.....e39ede413739c6aa7baae151ea5d7198 |
| Βάση Δεδομένων: | OpenAIRE |
| ISSN: | 15328007 10705511 |
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| DOI: | 10.1080/10705511.2023.2230519 |