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Activity Number: 377 - Nonparametric Saturated Methods to Handle Nonignorable Missing Data
Type: Invited
Date/Time: Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract #321921 View Presentation
Title: Parametric, Semiparametric and Non-Parametric Methods for Sensitivity Analysis with Incomplete Data
Author(s): Geert Molenberghs *
Companies: Universiteit Hasselt
Keywords: missing data ; incomplete data ; sensitivity analysis ; selection model ; pattern-mixture model ; shared-parameter model
Abstract:

The fundamental non-verifiable nature of the missing-data mechanism is discussed, in selection, pattern-mixture, and shared-parameter frameworks. It is shown how, in all three frameworks, MAR can be formulated to provide a useful analysis starting point. From there, parametric, semi-parametric, and non-parametric sensitivity analysis routes are discussed. It is indicated how non-parametric bounds can be usefully considered to provide a background for (semi-)parametric sensitivity analyses.


Authors who are presenting talks have a * after their name.

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