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Logistic Liu Estimator under stochastic linear restrictions

Author

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  • Nagarajah Varathan

    (University of Peradeniya
    University of Jaffna)

  • Pushpakanthie Wijekoon

    (University of Peradeniya)

Abstract

In order to overcome the problem of multicollinearity in logistic regression, several researchers proposed alternative estimators when exact linear restrictions are available in addition to sample model. However, in practical situations the linear restrictions are not always exact and mostly their nature is stochastic. In this paper, we propose a new estimator called stochastic restricted Liu maximum likelihood estimator (SRLMLE) by incorporating Liu estimator to the logistic regression model when the linear restrictions are stochastic. Moreover, the conditions for superiority of SRLMLE over the maximum likelihood estimator (MLE), stochastic restricted maximum likelihood estimator (SRMLE) and restricted Liu logistic estimator (RLLE) are derived with respect to mean square error criterion. Finally, the performance of the new estimator over MLE, LLE, SRMLE and RLLE is investigated in the sense of scalar mean squared error by conducting a Monte Carlo simulation and using a numerical example.

Suggested Citation

  • Nagarajah Varathan & Pushpakanthie Wijekoon, 2019. "Logistic Liu Estimator under stochastic linear restrictions," Statistical Papers, Springer, vol. 60(3), pages 945-962, June.
    • Handle: RePEc:spr:stpapr:v:60:y:2019:i:3:d:10.1007_s00362-016-0856-6
    DOI: 10.1007/s00362-016-0856-6
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. M. Revan Özkale & Atif Abbasi, 2022. "Iterative restricted OK estimator in generalized linear models and the selection of tuning parameters via MSE and genetic algorithm," Statistical Papers, Springer, vol. 63(6), pages 1979-2040, December.

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