Optimal determination of the parameters of some biased estimators using genetic algorithm

TEKELİ E., KAÇIRANLAR S., ÖZBAY N.

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, cilt.89, sa.18, ss.3331-3353, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 89 Sayı: 18
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1080/00949655.2019.1663848
  • Dergi Adı: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.3331-3353
  • Anahtar Kelimeler: Genetic algorithm, Liu estimator, parameter optimization, ridge estimator, two-parameter estimator, RIDGE-REGRESSION, OPTIMIZATION, SIMULATION, LIU, PERFORMANCE, GA
  • Çukurova Üniversitesi Adresli: Evet

Özet

In this paper, some new algorithms for estimating the biasing parameters of the ridge, Liu and two-parameter estimators are introduced with the help of genetic algorithm (GA). The proposed algorithms are based on minimizing some statistical measures such as mean square error (MSE), mean absolute error (MAE) and mean absolute prediction error (MAPE). At the same time, the new algorithms allow one to keep the condition number and variance inflation factors to be less than or equal to ten by means of the GA. A numerical example is presented to show the utility of the new algorithms. In addition, an extensive Monte Carlo experiment is conducted. The numerical findings prove that the proposed algorithms enable to eliminate the problem of multicollinearity and minimize the MSE, MAE and MAPE.