A Bayesian Vector Autoregressive Model with Nonignorable Missingness in Dependent Variables and Covariates: Development, Evaluation, and Application to Family Processes

• Published in: Structural equation modeling Vol. 27; no. 3; pp. 442 - 467
• Main Authors: Ji, Linying, Chen, Meng, Oravecz, Zita, Cummings, E. Mark, Lu, Zhao-Hua, Chow, Sy-Miin
• Format: Journal Article
• Language: English
• Published: United States Routledge 03.05.2020; Psychology Press
• Subjects: Bayesian analysis; Bayesian vector autoregressive model; Dependent variables; Intensive longitudinal data; Missing data; Multiple imputation; Nonignorable missing data; Time series; Intensive longitudinal data; Bayesian vector autoregressive model; Multiple imputation; Nonignorable missing data

Intensive longitudinal designs involving repeated assessments of constructs often face the problems of nonignorable attrition and selected omission of responses on particular occasions. However, time series models, such as vector autoregressive (VAR) models, are often fit to these data without consi...