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First-order r−d class estimator in binary logistic regression model

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  • Özkale, M. Revan
  • Arıcan, Engin

Abstract

In order to combat multicollinearity, a new biased estimator in the logistic regression model is introduced by combining principal component and Liu logistic estimators. The properties of the new estimator are discussed and the choice of the shrinkage parameter is proposed. Finally, a simulation study is done to compare the estimators.

Suggested Citation

  • Özkale, M. Revan & Arıcan, Engin, 2015. "First-order r−d class estimator in binary logistic regression model," Statistics & Probability Letters, Elsevier, vol. 106(C), pages 19-29.
    • Handle: RePEc:eee:stapro:v:106:y:2015:i:c:p:19-29
    DOI: 10.1016/j.spl.2015.06.021
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    References listed on IDEAS

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    1. Månsson, Kristofer & Kibria, B.M. Golam & Shukur, Ghazi, 2012. "On Liu estimators for the logit regression model," Economic Modelling, Elsevier, vol. 29(4), pages 1483-1488.
    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. Marx, Brian D., 1992. "A continuum of principal component generalized linear regressions," Computational Statistics & Data Analysis, Elsevier, vol. 13(4), pages 385-393, May.
    4. S. le Cessie & J. C. van Houwelingen, 1992. "Ridge Estimators in Logistic Regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(1), pages 191-201, March.
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