Imputation Of Missing Values In Clinical Data – Micha Johannes BIRKLBAUER
Micha Johannes BIRKLBAUER
Bachelor's thesis
Imputation Of Missing Values In Clinical Data
Imputation of missing values in clinical data
Abstract:
Imputation of missing data is a crucial step in data analysis since many statistical methods require complete datasets. In that regard MissForest imputation is a powerful tool that seems to outperform most other imputation approaches. This analysis evaluates how good imputation using MissForest is compared to other methods like imputation by Multivariate Imputation by Chained Equations (MICE), Restricted …moreAbstract:
Imputation of missing data is a crucial step in data analysis since many statistical methods require complete datasets. In that regard MissForest imputation is a powerful tool that seems to outperform most other imputation approaches. This analysis evaluates how good imputation using MissForest is compared to other methods like imputation by Multivariate Imputation by Chained Equations (MICE), Restricted …more
Language used: Czech
Date on which the thesis was submitted / produced: 3. 9. 2019
Thesis defence
- Date of defence: 9. 9. 2019
- Supervisor: doc. Dr. Ulrich Bodenhofer
Citation record
ISO 690-compliant citation record:
BIRKLBAUER, Micha Johannes. \textit{Imputation Of Missing Values In Clinical Data}. Online. Bachelor's thesis. České Budějovice: University of South Bohemia in České Budějovice, Faculty of Science. 2019. Available from: https://theses.cz/id/xibmdz/.
The right form of listing the thesis as a source quoted
BIRKLBAUER, Micha Johannes. Imputation Of Missing Values In Clinical Data. Č. Budějovice, 2019. bakalářská práce (Bc.). JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH. Přírodovědecká fakulta
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Institution archiving the thesis and making it accessible: JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH, Přírodovědecká fakultaUNIVERSITY OF SOUTH BOHEMIA IN ČESKÉ BUDĚJOVICE
Faculty of ScienceBachelor programme / field:
Applied Informatics / Bioinformatics
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