Subject Area
Statistics
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
Dissertation
Degree Name
Ph.D.
Department
Statistical Science
Advisor
Daniel F. Heitjan
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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