TY - JOUR TI - A Comparative Imputations Statistics Approaches With Application to Nigerian Rainfall Data AU - Taiwo Wale Ayanniyi AU - Efosa Michael Ogbeide PY - 2026 JO - Ktrend - International Journal of Mathematics and Statistics (IJMS) VL - 2 IS - 1 SP - 1-15 DO - 10.5281/zenodo.22985985 UR - https://doi.org/10.5281/zenodo.22985985 AB -

Missing values in rainfall datasets reduce the reliability of statistical inference and undermine decision-making in agriculture, climate studies, and environmental management. This study evaluated the performance of five imputation techniques; Expectation-Maximization (EM), Multiple Imputation (MI), Regression Imputation (RI), Bootstrap Expectation-Maximization (BEM), and Random Forest (RF) using the 2019 Nigerian rainfall dataset obtained from the National Bureau of Statistics. The methods were compared using Raw Bias, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Variance to assess estimation accuracy, predictive performance, and stability. The results revealed that MI produced the lowest bias (-0.003), making it the most suitable method for minimizing systematic estimation error. In contrast, RF achieved the highest predictive accuracy, recording the lowest MSE (0.9530) and RMSE (0.9762). Although BEM exhibited the lowest variance (0.9844), indicating greater stability, it was associated with relatively high bias, limiting its overall effectiveness. The findings demonstrate that no single method is universally optimal; rather, the choice of imputation technique should be guided by the primary analytical objective. RF is recommended for applications requiring high predictive accuracy, whereas MI is preferable when unbiased parameter estimation is essential. The study provides empirical evidence to support the adoption of robust imputation techniques by agencies such as the Nigerian Meteorological Agency (NiMet), thereby improving the quality of national climate databases and strengthening evidence-based agricultural and environmental decision-making.

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