Variable selection in Poisson regression model using Golden jackal optimization algorithm
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Abstract
Variable selection is a very helpful procedure for improving prediction accuracy by finding the most important variables that related to the response variable. Poisson regression model has received much attention in several science fields for modeling count data. Golden jackal optimization algorithm (GJO) is one of the recently efficient proposed nature-inspired algorithms that can efficiently be employed for variable selection. In this work, GJO algorithm is proposed to perform variable selection for Poisson regression model. Extensive simulation studies and real data application are conducted to evaluate the performance of the proposed method in terms of prediction accuracy and variable selection criteria. The results proved the efficiency of our proposed methods and it outperforms other popular methods.
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الراوي، خاشع محمود، (1978)،"مدخل الى تحليل الانحدار"، جامعة الموصل.
صبري، حسام موفق (2013)، "مقارنة طرائق تقدير معلمات انموذج انحدار بواسون في ظل وجود مشكلة التعدد الخطي مع تطبيق عملي"، أطروحة دكتوراه، غير منشورة، كلية الإدارة والاقتصاد، جامعة بغداد.
عبدالله، لقاء سعيد وعلوش، ذكرى علي و الجراح، إسراء عبد الحق ،(2011)، "دراسة إنزيم ميتالوإندوببتايديز وعلاقته بمرض العجز الكلوي المزمن"، مجلة علوم الرافدين، المجلد (22)، العدد (4)، ص (71-87).
Mehrabian, A. R., & Lucas, C. (2006). A novel numerical optimization algorithm inspired from weed colonization. Ecological informatics, 1(4), 355-366.
Algamal, Z. Y. and Lee, M. H. (2015). "Penalized Poisson Regression Model using adaptive modified Elastic Net Penalty". Electronic Journal of Applied Statistical Analysis,Vol. 08, Issue 02, 236-245.
El Anbari, M. and Mkhadri, A. (2013)." The adaptive gril estimator with a diverging number of parameters. Communications in Statistics - Theory and Methods". 42(14), 2634–2660.
Fan, J., & Li, R. (2001). "Variable selection via nonconcave penalized likelihood and its oracle properties". Journal of the American Statistical Association, 96(456), 1348-1360.
Hossain, S. and Ahmed, E. (2012). "Shrinkage and penalty estimators of a Poisson regression model". Australian & New Zealand Journal of Statistics. 54(3), 359–373.
James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). "An introduction to statistical learning". Springer, New York.
Månsson, K., Kibria, B. G., Sjolander, P & Shukur, G. (2012), "Improved Liu Estimators for the Poisson Regression Model", International Journal of Statistics and Probability, Vol. 1, No. 1, pp. 1-6.
Tibshirani, R. (1996). "Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society". Series B (Methodological). 58(1), 267–288.
Zou, H. (2006)." The adaptive lasso and its oracle properties. Journal of the American Statistical Association". 101(476), 1418–1429.
Zou, H. and Hastie, T. (2005). "Regularization and variable selection via the elastic net". Journal of the Royal Statistical Society. Series B (Methodological). 67(2), 301–320.