Variable selection in Inverse Gaussian regression model using modified crow search algorithm
Abstract
The inverse Gaussian regression model is one of the important models, which is widely used in many applications. The inverse Gaussian model is placed in tables of families of generalized linear models as it is one of the basic models. Like other regression models, the model may contain many independent variables, which negatively affects the accuracy of the model and its simplicity in interpreting the results. This study aims to use the modified crow search algorithm and compare it with other methods in selecting the variables in the inverse Gaussian regression model using simulation and real data. The results showed that the proposed method contributes to reducing the average square error of the model and achieves better performance compared to other previously used methods.
References
- Akram, M.N., et al., A new Liu-type estimator for the inverse Gaussian regression model. 2020. 90(7): p. 1153-1172.
- Algamal, Z.Y., & Lee, M. H. , A novel molecular descriptor selection method in QSAR classification model based on weighted penalized logistic regression. Journal of Chemometrics, 2017. 31(10).
- Alkhateeb, A.N. and Z.Y.J.E.J.o.A.S.A. Algamal, Variable selection in gamma regression model using chaotic firefly algorithm with application in chemometrics. 2021. 14(1): p. 266-276.
- Askarzadeh, A., A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Computers & Structures, 2016. 169: p. 1-12.
- Islam, M.J., X. Li, and Y. Mei, A time-varying transfer function for balancing the exploration and exploitation ability of a binary PSO. Applied Soft Computing, 2017. 59: p. 182-196.
- Mafarja, M., et al., Binary dragonfly optimization for feature selection using time-varying transfer functions. Knowledge-Based Systems, 2018. 161: p. 185-204.
- Mawlood, K.I., Estimating Hazard Function and Survival Analysis of Tuberculosis Patients in Erbil city. Tikrit Journal of Administration
- McCullagh, P. and J. Nelder, Generalized Linear Models. 1989, London: Chapman and Hall.
- Olsson, U., Generalized linear models. An applied approach. Studentlitteratur, Lund, 2002. 18.
- Peter K. D., G.K.S., Generalized Linear Models With Examples in R. 2018, New York: Springer.
- Ross, S.M., Introduction to probability and statistics for engineers and scientists. 2020: Academic press.
- Salh, A.P.D.S.M., et al., Using Multinomial Logistic Regression model to study factors that affect chest pain. 2021. 17(53 part 2).
- Sayed, G.I., A.E. Hassanien, and A.T. Azar, Feature selection via a novel chaotic crow search algorithm. Neural Computing and Applications, 2017.
- Yahya Algamal, Z., Performance of ridge estimator in inverse Gaussian regression model. Communications in Statistics-Theory
- yonis, F.a. and R.A. Othma, Shrinkage estimators in inverse Gaussian regression model: Subject review. IRAQI JOURNAL OF STATISTICAL SCIENCES, 2022. 19(35): p. 72-82.
