Discrete Choice Models for Nonmonotone Nonignorable Missing Data: Identification and Inference.

ABSTRACT: Nonmonotone missing data arise routinely in empirical studies of social and health sciences, and when ignored, can induce selection bias and loss of efficiency. In practice, it is common to account for nonresponse under a missing-at-random assumption which although convenient, is rarely appropriate when nonresponse is nonmonotone. Likelihood and Bayesian missing data methodologies often require specification of a parametric model for the full data law, thus a priori ruling out any prospect for semiparametric inference. In this paper, we propose an all-purpose approach which delivers semiparametric inferences when missing data are nonmonotone and not at random. The approach is based on a discrete choice model (DCM) as a means to generate a large class of nonmonotone nonresponse mecha

SUBMITTER: Tchetgen EJT 

PROVIDER: S-EPMC8118571 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature