Identification and Estimation of Graphical Models with Nonignorable Nonresponse

We study the identification and estimation of graphical models with nonignorable nonresponse. An observable variable correlated to nonresponse is added to identify the mean of response for the unidentifiable model. An approach to estimating the marginal mean of response is proposed, based on simulation imputation methods which are introduced for a variety of models including linear, generalized linear, and monotone nonlinear models. The proposed mean estimators are root N-consistent, where N is the sample size. Finite sample simulations confirm the effectiveness of the proposed method. Sensitivity analysis for the untestable assumption on our augmented model is also conducted. A real data example is employed to illustrate the use of the proposed methodology.

Automated summary

This study focuses on the identification and estimation of graphical models with nonignorable nonresponse. The proposed method introduces a new observable variable to identify the mean of response for the unidentifiable model and suggests a simulation imputation approach to estimate the marginal mean of response. The root N-consistent mean estimators show effectiveness in finite sample simulations and a real data example is used to illustrate the methodology.