Degree Type

Dissertation

Date of Award

2013

Degree Name

Doctor of Philosophy

Department

Statistics

First Advisor

Alicia L. Carriquiry

Second Advisor

Yongming Qu

Abstract

Missing data is a common problem in data analysis, and has been studied extensively. We propose using simulation extrapolation(SIMEX), a general simulation-based approach to adjust the bias in the estimator due to missing values assuming the model for missingness is known. The SIMEX approach was originally proposed for measurement error models. The SIMEX method includes simulation steps that use information from the missing mechanism and an extrapolation step to adjust the bias. While EM and multiple imputation methods rely on the correct assumptions on the conditional distribution of missing data given observed data, the proposed SIMEX method assumes the correct model for the missingness. Therefore, SIMEX is more robust on an incorrect specification of the probability model for the unobserved data. We discuss the properties of the SIMEX estimator and compare this method with existing methods using

simulation. The advantages and limitations of our approach are also discussed.

DOI

https://doi.org/10.31274/etd-180810-2282

Copyright Owner

Yu-Yi Hsu

Language

en

File Format

application/pdf

File Size

134 pages

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