Abstract
In this dissertation, we explore sensitivity analyses under three different types of incomplete data problems, including missing outcomes, missing outcomes and missing predictors, potential outcomes in \emph{Rubin causal model (RCM)}. The first sensitivity analysis is conducted for the \emph{missing completely at random (MCAR)} assumption in frequentist inference; the second one is conducted for the \emph{missing at random (MAR)} assumption in likelihood inference; the third one is conducted for one novel assumption, the ``sixth assumption'' proposed for the robustness of instrumental variable estimand in causal inference.
Degree Date
Spring 5-16-2020
Document Type
Thesis
Degree Name
Ph.D.
Department
Statistical Science
Advisor
Daniel F. Heitjan
Subject Area
Statistics
Format
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Chen, Heng, "Sensitivity Analysis for Incomplete Data and Causal Inference" (2020). Statistical Science Theses and Dissertations. 14.
https://scholar.smu.edu/hum_sci_statisticalscience_etds/14
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