Evaluating the Performance of Bayesian Approach for Imputing Missing Data under different Missingness Mechanism

In the realm of data analysis, missing data pose a significant challenge, requiring robust imputation methods to ensure the integrity and reliability of results. This study delves into the performance evaluation of a Bayesian approach for imputing missing data across various missingness mechanisms. The investigation encompasses different scenarios of missing data patterns, shedding light on the adaptability and efficacy of Bayesian techniques. Performance metrics were carefully selected to measure the efficacy of the Bayesian approach under various scenarios. Through a comparative analysis, this research aims to unveil the strengths and limitations of Bayesian imputation in handling diverse missing data challenges. Findings of this study contribute valuable insights for researchers to effectively employ Bayesian imputation techniques, particularly when faced with varying missing data patterns.