1IGNORANCE AND UNCERTAINTY REGIONS AS INFERENTIAL TOOLS IN A SENSITIVITY ANALYSIS

Abstract: It has long been recognised that most standard point estimators lean heavily on untestable assumptions when missing data have occurred. Statisticians have therefore advocated the use of sensitivity analysis, but payed relatively little attention to strategies for summarizing the results from such analyses, which have clear interpretation, verifiable properties and feasible implementation. As a step in this direction, several authors have proposed to shift the focus of inference from point estimators to estimated intervals or regions of ignorance. These regions com-bine standard point estimates obtained under all possible/plausible missing data models that yield identified parameters of interest. They thus reflect the achiev-able information from the given data generation structure with its missing data component. The standard framework of inference needs extension to allow for a transparent study of statistical properties of such regions. In this paper we propose a definition of consistency for the region and intro-duce the concepts of pointwise, weak and strong coverage for larger regions which acknowledge sampling imprecision in addition to the structural lack of information