Skip to main content

Fitting multilevel multivariate models with missing data in responses and covariates that may include interactions and non‐linear terms

Buy Article:

$51.00 plus tax (Refund Policy)


The paper extends existing models for multilevel multivariate data with mixed response types to handle quite general types and patterns of missing data values in a wide range of multilevel generalized linear models. It proposes an efficient Bayesian modelling approach that allows missing values in covariates, including models where there are interactions or other functions of covariates such as polynomials. The procedure can also be used to produce multiply imputed complete data sets. A simulation study is presented as well as the analysis of a longitudinal data set. The paper also shows how existing multiprocess models for handling endogeneity can be extended by the framework proposed.

Keywords: Endogeneity; Latent normal model; Markov chain Monte Carlo methods; Missing data; Multilevel modelling; Multiple imputation; Multiprocess model; Multivariate modelling

Document Type: Research Article


Publication date: February 1, 2014


Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
Cookie Policy
ingentaconnect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more
Real Time Web Analytics