Two parameter Ridge estimator in the inverse Gaussian regression model

Bulut Y. M., Işılar M.

HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS, cilt.50, sa.3, ss.895-910, 2021 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 50 Sayı: 3
  • Basım Tarihi: 2021
  • Doi Numarası: 10.15672/hujms.813540
  • Dergi Adı: HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, zbMATH, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.895-910
  • Anahtar Kelimeler: inverse Gaussian regression, biased estimators, two parameter Ridge estimator, multicollinearity, MEAN-SQUARE ERROR, PERFORMANCE
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

It is well known that multicollinearity, which occurs among the explanatory variables, has adverse effects on the maximum likelihood estimator in the inverse Gaussian regression model. Biased estimators are proposed to cope with the multicollinearity problem in the inverse Gaussian regression model. The main interest of this article is to introduce a new biased estimator. Also, we compare newly proposed estimator with the other estimators given in the literature. We conduct a Monte Carlo simulation and provide a real data example to illustrate the performance of the proposed estimator over the maximum likelihood and Ridge estimators. As a result of the simulation study and real data example, the newly proposed estimator is superior to the other estimators used in this study.