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, cilt.63, sa.6, ss.1979-2040, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 63 Sayı: 6
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1007/s00362-022-01304-0
  • Dergi Adı: STATISTICAL PAPERS
  • Derginin Tarandığı İndeksler: 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
  • Sayfa Sayıları: ss.1979-2040
  • Anahtar Kelimeler: Liu estimator, OK estimator, Ridge estimator, Two parameter estimator, Exact restrictions, Genetic algorithm, RIDGE-REGRESSION, 2-PARAMETER ESTIMATOR, POISSON, PERFORMANCE, SIMULATION
  • Çukurova Üniversitesi Adresli: Evet

Özet

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.