Degree Name
MS (Master of Science)
Program
Mathematical Sciences
Date of Award
5-2019
Committee Chair or Co-Chairs
Christina Nicole Lewis
Committee Members
Robert M. Price Jr., JeanMarie L. Hendrickson
Abstract
A statistician's job is to produce statistical models. When these models are precise and unbiased, we can relate them to new data appropriately. However, when data sets have missing values, assumptions to statistical methods are violated and produce biased results. The statistician's objective is to implement methods that produce unbiased and accurate results. Research in missing data is becoming popular as modern methods that produce unbiased and accurate results are emerging, such as MICE in R, a statistical software. Using real data, we compare four common imputation methods, in the MICE package in R, at different levels of missingness. The results were compared in terms of the regression coefficients and adjusted R^2 values using the complete data set. The CART and PMM methods consistently performed better than the OTF and RF methods. The procedures were repeated on a second sample of real data and the same conclusions were drawn.
Document Type
Thesis - unrestricted
Recommended Citation
Heidt, Kaitlyn, "Comparison of Imputation Methods for Mixed Data Missing at Random" (2019). Electronic Theses and Dissertations. Paper 3559. https://dc.etsu.edu/etd/3559
Copyright
Copyright by the authors.