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MIBEN: Robust Multiple Imputation with the Bayesian Elastic Net
Lang, Kyle Matthew
Lang, Kyle Matthew
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Abstract
Correctly specifying the imputation model when conducting multiple imputation remains one of the most significant challenges in missing data analysis. This dissertation introduces a robust multiple imputation technique, Multiple Imputation with the Bayesian Elastic Net (MIBEN), as a remedy for this difficulty. A Monte Carlo simulation study was conducted to assess the performance of the MIBEN technique and compare it to several state-of-the-art multiple imputation methods.
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Date
2015-05-31
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University of Kansas
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Keywords
Quantitative psychology and psychometrics, Statistics, Bayesian Statistics, Big Data, Missing Data, Multiple Imputation, P > N, Regularized Regression