A Bayesian Perspective on Assessing Sensitivity to Assumptions about Unobserved Data

Authored by: W. Hogan Joseph , J. Daniels Michael , Liangyuan Hu

Missing Data Methodology

Print publication date:  November  2014
Online publication date:  November  2014

Print ISBN: 9781439854617
eBook ISBN: 9781439854624
Adobe ISBN:

10.1201/9781439854624-24

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Abstract

This chapter provides a Bayesian perspective on how inference might proceed in settings where data that are intended to be collected are missing. Assessment of model sensitivity is a broad topic, encompassing many aspects of inference that might include distributional assumptions, parametric structure, sensitivity to outliers, and assessment of influence of individual data points. When the intended sample is completely observed, many of these modeling assumptions can be checked empirically; our ability to refute the assumptions with any degree of confidence is limited only by sample size, so in some sense these assumptions can be subjected to empirical critique. Assumptions required for fitting models to incomplete data are different because they apply to data that cannot be observed and are therefore inherently untestable. Put simply, they are subjective.

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