Proceedings of International Conference on Applied Innovation in IT  ·  2026/03/31  ·  Vol. 14  ·  Issue 1  ·  pp. 97–105
Osprey Optimization for Influence Diagnostics in Regression Models
Luay Adil Abduljabbar and Sabah Manfi Ridha
📄 Download PDF DOI: 10.25673/123537
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.
References
  1. E. Nunez, E. W. Steyerberg, and J. Nunez, “Regression modeling strategies,” Revista Española de Cardiología (English Edition), vol. 64, no. 6, pp. 501-507, 2011.
  2. Z. Y. Algamal, “Developing a ridge estimator for the gamma regression model,” Journal of Chemometrics, vol. 32, no. 10, p. e3054, 2018.
  3. E. M. Ortega, V. G. Cancho, and G. A. Paula, “Generalized log-gamma regression models with cure fraction,” Lifetime Data Analysis, vol. 15, no. 1, pp. 79-106, 2009.
  4. Y. Asar and Z. Algamal, “A new two-parameter estimator for the gamma regression model,” Statistics, Optimization & Information Computing, vol. 10, no. 3, pp. 750-761, 2022.
  5. I. Dawoud, “A new improved estimator for the gamma regression model,” Communications in Statistics - Simulation and Computation, pp. 1-12, 2025.
  6. E. Dunder, S. Gumustekin, and M. A. Cengiz, “Variable selection in gamma regression models via artificial bee colony algorithm,” Journal of Applied Statistics, vol. 45, no. 1, pp. 8-16, 2018.
  7. N. A. Al-Thanoon, O. S. Qasim, and Z. Y. Algamal, “Variable selection in Gamma regression model using binary gray Wolf optimization algorithm,” in Journal of Physics: Conference Series, IOP Publishing, 2020.
  8. P. W. Bernhardt, “Model validation and influence diagnostics for regression models with missing covariates,” Statistics in Medicine, vol. 37, no. 8, pp. 1325-1342, 2018.
  9. A. Tapia, et al., “Influence diagnostics in mixed effects logistic regression models,” Test, vol. 28, no. 3, pp. 920-942, 2019.
  10. S. Liu and A. H. Welsh, “Regression diagnostics,” in International Encyclopedia of Statistical Science, Springer, 2025, pp. 2151-2156.
  11. B. Rajaratnam, et al., “Influence diagnostics for high-dimensional lasso regression,” Journal of Computational and Graphical Statistics, vol. 28, no. 4, pp. 877-890, 2019.
  12. J. V. Oliveira Jr, F. Cribari-Neto, and J. S. Nobre, “Influence diagnostics and model validation for the generalized extreme-value nonlinear regression model,” Journal of Statistical Computation and Simulation, vol. 90, no. 3, pp. 515-549, 2020.
  13. J. N. da Cruz, E. M. Ortega, and G. M. Cordeiro, “The log-odd log-logistic Weibull regression model: modelling, estimation, influence diagnostics and residual analysis,” Journal of Statistical Computation and Simulation, vol. 86, no. 8, pp. 1516-1538, 2016.
  14. Z. Y. Algamal, “Diagnostic in poisson regression models,” Electronic Journal of Applied Statistical Analysis, vol. 5, no. 2, pp. 178-186, 2012.
  15. M. Amin, M. Amanullah, and G. M. Cordeiro, “Influence diagnostics in the gamma regression model with adjusted deviance residuals,” Communications in Statistics - Simulation and Computation, vol. 46, no. 9, pp. 6959-6973, 2017.
  16. N. A. Al-Thanoon, Z. Y. Algamal, and O. S. Qasim, “Feature selection based on a crow search algorithm for big data classification,” Chemometrics and Intelligent Laboratory Systems, vol. 212, 2021.
  17. A. J. Lemonte and A. G. Patriota, “Influence diagnostics in Birnbaum-Saunders nonlinear regression models,” Journal of Applied Statistics, vol. 38, no. 5, pp. 871-884, 2011.
  18. M. Amin, M. Amanullah, M. Aslam, and M. Qasim, “Influence diagnostics in gamma ridge regression model,” Journal of Statistical Computation and Simulation, vol. 89, no. 3, pp. 536-556, 2019.
  19. A. Khan, M. Amanullah, M. Amin, R. Alharbi, A. H. Muse, and M. S. Mohamed, “Influence diagnostic methods in the Poisson regression model with the Liu estimator,” Computational Intelligence and Neuroscience, vol. 2021, no. 1, p. 4407328, 2021.
  20. N. Saleem, A. Akbar, S. Laeeq, and S. Ahmad, “Influential observations detection in the gamma-pareto regression model under different link functions with standardized and adjusted deviance residuals: simulation and application,” Sigma: Journal of Engineering and Natural Sciences, vol. 43, no. 3, 2025.
  21. M. Amin, M. Amanullah, and G. M. Cordeiro, “Influence diagnostics in the gamma regression model with adjusted deviance residuals,” Communications in Statistics - Simulation and Computation, vol. 46, no. 9, pp. 6959-6973, 2017.
  22. A. Zakaria, N. K. Howard, and B. K. Nkansah, “On the detection of influential outliers in linear regression analysis,” 2014.
  23. E. Uusipaikka, Confidence intervals in generalized regression models. Chapman & Hall/CRC Press, 2009.
  24. M. Amin, M. Amanullah, and G. M. Cordeiro, “Influence diagnostics in the Gamma regression model with adjusted deviance residuals,” Communications in Statistics - Simulation and Computation, vol. 46, no. 9, pp. 6959-6973, 2017.
  25. M. Dehghani and P. Trojovský, “Osprey optimization algorithm: A new bio-inspired metaheuristic algorithm for solving engineering optimization problems,” Frontiers in Mechanical Engineering, vol. 8, p. 1126450, 2023.
  26. R. A. Grove, C. J. Henny, and J. L. Kaiser, “Osprey: worldwide sentinel species for assessing and monitoring environmental contamination in rivers, lakes, reservoirs, and estuaries,” Journal of Toxicology and Environmental Health, Part B, vol. 12, no. 1, pp. 25-44, 2009.
  27. A. F. Poole, R. O. Bierregaard, and M. S. Martell, Osprey: Pandion Haliaetus. Birds of North America, Incorporated, 2002.

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