Proceedings of International Conference on Applied Innovation in IT
2026/03/31, Volume 14, Issue 1, pp.97-105
Osprey Optimization for Influence Diagnostics in Regression Models
Luay Adil Abduljabbar and Sabah Manfi Ridha Abstract: Diagnosing of influence is an originality that is necessary to help identify influential observations, which affect inference, particularly estimation of the model. Diagnostics such as Cooks Distance and DFFITS are well-known classical diagnostic tools that may turn out to be inappropriate or not in complexities of models or at varying category dispersion of data. In the present article, a new attempt to extend the field of influence diagnostics to the Gamma regression models (GRM) is attempted to apply the metaheuristic algorithm referred to as the Osprey Optimization Algorithm (OOA). In order to compare the GRM detecting power of Cook s Distance and DFFITS as well as the OOA, we perform a far-reaching simulation study of the two sample sizes and dispersion parameters. The simulation outcomes confirm that the Cook Distance as well as the DFFITS are effective yet OOA-enhanced diagnostic scheme is superior to identify influential cases especially in conditions of high dispersion and in-limited to medium sample. When described through the prism of the compared analysis, one may claim OOA is more comprehensive in terms of the detection mechanism.
Keywords: Cooks Distance, DFFITS, Gamma Regression Model, Osprey Optimization Algorithm.
DOI: Under indexing
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