A Bayesian Vector Autoregressive Model with Nonignorable Missingness in Dependent Variables and Covariates: Development, Evaluation, and Application to Family Processes-Reference-Cited by-同舟云学术

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

Published:2019-07-18 Issue:3 Volume:27 Page:442-467
ISSN:1070-5511
Container-title:Structural Equation Modeling: A Multidisciplinary Journal
language:en
Short-container-title:Structural Equation Modeling: A Multidisciplinary Journal

Author:

Ji Linying¹,Chen Meng¹,Oravecz Zita¹,Cummings E. Mark²,Lu Zhao-Hua³,Chow Sy-Miin¹

Affiliation:

1. The Pennsylvania State University

2. University of Notre Dame

3. St. Jude Children’s Research Hospital

Funder

National Center for Advancing Translational Sciences

National Institutes of Health

Penn State Quantitative Social Sciences Initiative

ntensive Longitudinal Health Behavior Cooperative Agreement Program funded by the National Institutes of Health

National Science Foundation

Publisher

Informa UK Limited

Subject

General Economics, Econometrics and Finance,Sociology and Political Science,Modeling and Simulation,General Decision Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/10705511.2019.1623681

Reference72 articles.

1. Estimation of Linear Models with Incomplete Data

2. Missing Data Techniques for Structural Equation Modeling.

3. Network Analysis: An Integrative Approach to the Structure of Psychopathology

4. DeCon: A tool to detect emotional concordance in multivariate time series data of emotional responding

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