Missing Data Imputation Using Bayesian Classifier
S.Veerakannan Deputy Librarian, NGM College, Pollachi, Tamilnadu ngmcollegelibrary@gmail.com
Keywords:
Missing Data, Bayesian ClassifierAbstract
Dealing with missing data is a pervasive challenge in statistical analysis and machine learning. Multiple imputation has emerged as a valuable strategy for handling incomplete datasets, enabling analysts to derive valid statistical inferences that accurately reflect the uncertainty associated with missing values. This paper explores various methodologies for analyzing missing data, with a focus on the application of multiple imputation techniques and the integration of advanced supervised machine learning algorithms, specifically the Bayesian Classifier and Booster Algorithm. Furthermore, we introduce new procedures implemented in SAS R for generating multiple imputations for incomplete multivariate data and properly analyzing the results obtained from these data sets.
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References
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.
Horton, N. J., & Kleinman, K. (2007). Much ado about nothing: A comparison of missing data methods and software to fit incomplete data regression models. The American Statistician, 61(1), 3-24. https://doi.org/10.1198/000313007X172556
Little, R. J. A., & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley.
Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. John Wiley & Sons.
Van Buuren, S. (2018). Flexible Imputation of Missing Data (2nd ed.). CRC Press.
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Copyright (c) 2024 S Veerakannan, Deputy Librarian (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
