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2022 | 23 | nr 3 | 113--126
Tytuł artykułu

An Improved Ridge Type Estimator for Logistic Regression

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, an improved ridge type estimator is introduced to overcome the effect of multicollinearity in logistic regression. The proposed estimator is called a modified almost unbiased ridge logistic estimator. It is obtained by combining the ridge estimator and the almost unbiased ridge estimator. In order to asses the superiority of the proposed estimator over the existing estimators, theoretical comparisons based on the mean square error and the scalar mean square error criterion are presented. A Monte Carlo simulation study is carried out to compare the performance of the proposed estimator with the existing ones. Finally, a real data example is provided to support the findings. (original abstract)
Rocznik
Tom
23
Numer
Strony
113--126
Opis fizyczny
Twórcy
  • University of Jaffna, Sri Lanka
Bibliografia
  • Aguilera, A. M., Escabias, M., Valderrama, M. J., (2006). Using principal components for estimating logistic regression with high-dimensional multicollinear data. Computational Statistics & Data Analysis, 50, pp. 1905-1924.
  • Asar, Y., Genç, A., (2016). New Shrinkage Parameters for the Liu-type Logistic Estimators. Communications in Statistics- Simulation and Computation, 45(3), pp. 1094-1103.
  • Farebrother, R. W., (1976). Further results on the mean square error of ridge regression. J. R. Stat. Soc. Ser B., 38, pp. 248-250.
  • Inan, D., Erdogan, B. E., (2013). Liu-Type logistic estimator. Communications in Statistics- Simulation and Computation, 42, pp. 1578-1586.
  • Jadhav, N. H., (2020). On linearized ridge logistic estimator in the presence of multicollinearity. Comput Stat., 35, pp. 667-687.
  • Kibria, B. M. G., (2003). Performance of some new ridge regression estimators.Commun. Statist. Theor. Meth., 32, pp. 419-435.
  • Lukman, A.F., Emmanuel, A., Clement, O.A. et al., (2020). A Modified Ridge-Type Logistic Estimator. Iran J Sci Technol Trans Sci., 44, pp. 437-443.
  • Mansson, G., Kibria, B. M. G., Shukur, G., (2012). On Liu estimators for the logit regression model. The Royal Institute of Techonology, Centre of Excellence for Science and Innovation Studies (CESIS), Sweden, Paper No. 259.
  • McDonald, G. C., and Galarneau, D. I., (1975). A Monte Carlo evaluation of some ridge type estimators. Journal of the American Statistical Association, 70, pp. 407-416.
  • Nja, M. E., Ogoke, U. P., Nduka, E. C., (2013). The logistic regression model with a modified weight function. Journal of Statistical and Econometric Method, Vol. 2, No. 4, pp. 161-171.
  • Rao, C. R.,and Toutenburg, H.,(1995). Linear Models:Least Squares and Alternatives, Second Edition .Springer-Verlag New York, Inc.
  • Rao, C. R., Toutenburg, H., Shalabh and Heumann, C., (2008). Linear Models and Generalizations. Springer. Berlin.
  • Schaefer, R. L., Roi, L. D., Wolfe, R. A., (1984). A ridge logistic estimator. Commun. Statist. Theor. Meth, 13, pp. 99-113.
  • Trenkler, G. and Toutenburg, H., (1990). Mean Square Error Matrix Comparisons between Biased Estimators-An Overview of Recent Results. Statistical Papers, 31, pp. 165- 179, http://dx.doi.org/10.1007/BF02924687.
  • Varathan, N., Wijekoon, P., (2019). Modified almost unbiased Liu estimator in logistic regression. Communications in Statistics - Simulation and Computation, DOI: 10.1080/03610918.2019.1626888.
  • Wu, J., Asar, Y., (2016). On almost unbiased ridge logistic estimator for the logistic regression model. Hacettepe Journal of Mathematics and Statistics, 45(3), pp. 989-998, DOI: 10.15672/HJMS.20156911030.
  • Xinfeng, C., (2015). On the almost unbiased ridge and Liu estimator in the logistic regression model. International Conference on Social Science, Education Management and Sports Education. Atlantis Press, pp. 1663-1665.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171662716

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