Essential Aspects of Bayesian Data Imputation

30 Pages Posted: 20 Jul 2023 Last revised: 12 Dec 2023

See all articles by William Holt

William Holt

Marist College - Department of Mathematics

Duy Nguyen

Marist College - Department of Mathematics

Date Written: June 28, 2023

Abstract

Data imputation holds significant importance in a variety of fields including risk management. Incomplete or missing data can hinder a thorough analysis of risks, making accurate decision-making challenging. By employing imputation techniques to fill in the gaps, risk managers can obtain a more comprehensive and reliable understanding of the underlying risk factors. This, in turn, enables them to make informed decisions and develop effective strategies for risk mitigation. This note introduces the concept Bayesian data imputation. We collect and provide backgrounds needed for Bayesian data imputation when missing data are missing at random. Numerical examples are provided for demonstration.

Suggested Citation

Holt, William and Nguyen, Duy, Essential Aspects of Bayesian Data Imputation (June 28, 2023). Available at SSRN: https://ssrn.com/abstract=4494314 or http://dx.doi.org/10.2139/ssrn.4494314

William Holt

Marist College - Department of Mathematics

Duy Nguyen (Contact Author)

Marist College - Department of Mathematics ( email )

NY
United States

HOME PAGE: http://sites.google.com/site/nducduy/

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