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Optimal stochastic restricted logistic estimator

Author

Listed:
  • Nagarajah Varathan

    (University of Peradeniya
    University of Jaffna)

  • Pushpakanthie Wijekoon

    (University of Peradeniya)

Abstract

It is well known that the use of prior information in the logistic regression improves the estimates of regression coefficients when multicollinearity presents. This prior information may be in the form of exact or stochastic linear restrictions. In this article, in the presence of stochastic linear restrictions, we propose a new efficient estimator, named Stochastic restricted optimal logistic estimator for the parameters in the logistic regression models when the multicollinearity presents. Further, conditions for the superiority of the new optimal estimator over some existing estimators are derived with respect to the mean square error matrix sense. Moreover, a Monte Carlo simulation study and a real data example are provided to illustrate the performance of the proposed optimal estimator in the scalar mean square error sense.

Suggested Citation

  • Nagarajah Varathan & Pushpakanthie Wijekoon, 2021. "Optimal stochastic restricted logistic estimator," Statistical Papers, Springer, vol. 62(2), pages 985-1002, April.
    • Handle: RePEc:spr:stpapr:v:62:y:2021:i:2:d:10.1007_s00362-019-01121-y
    DOI: 10.1007/s00362-019-01121-y
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    References listed on IDEAS

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    1. Jibo Wu, 2016. "Modified restricted Liu estimator in logistic regression model," Computational Statistics, Springer, vol. 31(4), pages 1557-1567, December.
    2. Aguilera, Ana M. & Escabias, Manuel & Valderrama, Mariano J., 2006. "Using principal components for estimating logistic regression with high-dimensional multicollinear data," Computational Statistics & Data Analysis, Elsevier, vol. 50(8), pages 1905-1924, April.
    3. U.P. Ogoke & E.C. Nduka & M.E. Nja, 2013. "A New Logistic Ridge Regression Estimator Using Exponentiated Response Function," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 2(4), pages 1-12.
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