| dc.contributor.advisor | Wu, Wei | |
| dc.contributor.author | Lang, Kyle Matthew | |
| dc.date.accessioned | 2015-12-03T04:22:57Z | |
| dc.date.available | 2015-12-03T04:22:57Z | |
| dc.date.issued | 2015-05-31 | |
| dc.date.submitted | 2015 | |
| dc.identifier.other | http://dissertations.umi.com/ku:14018 | |
| dc.identifier.uri | http://hdl.handle.net/1808/19062 | |
| dc.description.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. | |
| dc.format.extent | 106 pages | |
| dc.language.iso | en | |
| dc.publisher | University of Kansas | |
| dc.rights | Copyright held by the author. | |
| dc.subject | Quantitative psychology and psychometrics | |
| dc.subject | Statistics | |
| dc.subject | Bayesian Statistics | |
| dc.subject | Big Data | |
| dc.subject | Missing Data | |
| dc.subject | Multiple Imputation | |
| dc.subject | P > N | |
| dc.subject | Regularized Regression | |
| dc.title | MIBEN: Robust Multiple Imputation with the Bayesian Elastic Net | |
| dc.type | Dissertation | |
| dc.contributor.cmtemember | Deboeck, Pascal R. | |
| dc.contributor.cmtemember | Woods, Carol M. | |
| dc.contributor.cmtemember | Johnson, Paul E. | |
| dc.contributor.cmtemember | Skorupski, William P. | |
| dc.thesis.degreeDiscipline | Psychology | |
| dc.thesis.degreeLevel | Ph.D. | |
| dc.identifier.orcid | https://orcid.org/0000-0001-5340-7849 | |
| dc.rights.accessrights | openAccess | |
