Restricted ridge estimator in generalized linear models: Monte Carlo simulation studies on Poisson and binomial distributed responses

KURTOĞLU F., Ozkale M. R.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, cilt.48, sa.4, ss.1191-1218, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 48 Sayı: 4
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1080/03610918.2017.1408822
  • Dergi Adı: COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1191-1218
  • Anahtar Kelimeler: Generalized linear models, Mean squared error, Multicollinearity, Poisson distribution, Restricted ridge estimation, REGRESSION, PARAMETERS
  • Çukurova Üniversitesi Adresli: Evet

Özet

It is known that collinearity among the explanatory variables in generalized linear models (GLMs) inflates the variance of maximum likelihood estimators. To overcome multicollinearity in GLMs, ordinary ridge estimator and restricted estimator were proposed. In this study, a restricted ridge estimator is introduced by unifying the ordinary ridge estimator and the restricted estimator in GLMs and its mean squared error (MSE) properties are discussed. The MSE comparisons are done in the context of first-order approximated estimators. The results are illustrated by a numerical example and two simulation studies are conducted with Poisson and binomial responses.