Nonstandard conditionally specified models for nonignorable missing data.

ABSTRACT: Data analyses typically rely upon assumptions about the missingness mechanisms that lead to observed versus missing data, assumptions that are typically unassessable. We explore an approach where the joint distribution of observed data and missing data are specified in a nonstandard way. In this formulation, which traces back to a representation of the joint distribution of the data and missingness mechanism, apparently first proposed by J. W. Tukey, the modeling assumptions about the distributions are either assessable or are designed to allow relatively easy incorporation of substantive knowledge about the problem at hand, thereby offering a possibly realistic portrayal of the data, both observed and missing. We develop Tukey's representation for exponential-family models, propose a comp

SUBMITTER: Franks AM 

PROVIDER: S-EPMC7430986 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature