This paper presents a comprehensive comparison of various missing data approaches in gamma regression analysis. The study evaluates the performance of linear trend at point method, mean imputation method, and three multiple imputation methods (KNN, PMM, and EM) in handling missing data at different positions (top, center, and bottom) of the data range. The maximum likelihood estimation technique is employed to estimate the parameters of the gamma regression model. An empirical example is presented to demonstrate the application of these methods in analyzing factors affecting carbon dioxide emission in Egypt. The findings reveal that multiple imputation methods outperform other approaches in terms of accuracy and precision. This study provides valuable insights into how different missing data techniques can be utilized to enhance the accuracy and precision of gamma regression models. The results have important implications for researchers and practitioners who use gamma regression analysis to investigate various phenomena with missed data.
Eldesokey, Amira. (2024). THE IMPACT OF HANDLING MISSED DATA ON THE GAMMA REGRESSION RESPONSE. المجلة العلمية للدراسات والبحوث المالية والتجارية, 5(1), 273-303. doi: 10.21608/cfdj.2024.324098
MLA
Amira Eldesokey. "THE IMPACT OF HANDLING MISSED DATA ON THE GAMMA REGRESSION RESPONSE". المجلة العلمية للدراسات والبحوث المالية والتجارية, 5, 1, 2024, 273-303. doi: 10.21608/cfdj.2024.324098
HARVARD
Eldesokey, Amira. (2024). 'THE IMPACT OF HANDLING MISSED DATA ON THE GAMMA REGRESSION RESPONSE', المجلة العلمية للدراسات والبحوث المالية والتجارية, 5(1), pp. 273-303. doi: 10.21608/cfdj.2024.324098
VANCOUVER
Eldesokey, Amira. THE IMPACT OF HANDLING MISSED DATA ON THE GAMMA REGRESSION RESPONSE. المجلة العلمية للدراسات والبحوث المالية والتجارية, 2024; 5(1): 273-303. doi: 10.21608/cfdj.2024.324098
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