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

Özkale M. R., Abbasi A.

STATISTICAL PAPERS, vol.63, no.6, pp.1979-2040, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 63 Issue: 6
  • Publication Date: 2022
  • Doi Number: 10.1007/s00362-022-01304-0
  • Journal Name: STATISTICAL PAPERS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, IBZ Online, International Bibliography of Social Sciences, ABI/INFORM, Aerospace Database, Business Source Elite, Business Source Premier, Communication Abstracts, EconLit, zbMATH
  • Page Numbers: pp.1979-2040
  • Keywords: Liu estimator, OK estimator, Ridge estimator, Two parameter estimator, Exact restrictions, Genetic algorithm, RIDGE-REGRESSION, 2-PARAMETER ESTIMATOR, POISSON, PERFORMANCE, SIMULATION
  • Çukurova University Affiliated: Yes

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.