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Iterative restricted OK estimator in generalized linear models and the selection of tuning parameters via MSE and genetic algorithm

Author

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  • M. Revan Özkale

    (Çukurova University)

  • Atif Abbasi

    (Çukurova University
    The University of Azad Jammu and Kashmir Muzaffarabad)

Abstract

This article introduces an iterative restricted OK estimator in generalized linear models to address the dilemma of multicollinearity by imposing exact linear restrictions on the parameters. It is a versatile estimator, which contains maximum likelihood (ML), restricted ML, Liu, restricted Liu, ridge and restricted ridge estimators in generalized linear models. To figure out the performance of restricted OK estimator over its counterparts, various comparisons are given where the performance evaluation criterion is the scalar mean square error (SMSE). Thus, illustrations and simulation studies for Gamma and Poisson responses are conducted apart from theoretical comparisons to see the performance of the estimators in terms of estimated and predicted MSE. Besides, the optimization techniques are applied to find the values of tuning parameters by minimizing SMSE and by using genetic algorithm.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:stpapr:v:63:y:2022:i:6:d:10.1007_s00362-022-01304-0
    DOI: 10.1007/s00362-022-01304-0
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    References listed on IDEAS

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