A Bayesian Latent Variable Selection Model for Nonignorable Missingness.

ABSTRACT: Missing data are exceedingly common across a variety of disciplines, such as educational, social, and behavioral science areas. Missing not at random (MNAR) mechanism where missingness is related to unobserved data is widespread in real data and has detrimental consequence. However, the existing MNAR-based methods have potential problems such as leaving the data incomplete and failing to accommodate incomplete covariates with interactions, non-linear terms, and random slopes. We propose a Bayesian latent variable imputation approach to impute missing data due to MNAR (and other missingness mechanisms) and estimate the model of substantive interest simultaneously. In addition, even when the incomplete covariates involves interactions, non-linear terms, and random slopes, the proposed method

SUBMITTER: Du H 

PROVIDER: S-EPMC10170967 | biostudies-literature | 2022 Mar-May

REPOSITORIES: biostudies-literature