A Jackknifed estimators for the negative binomial regression model


TÜRKAN S. , Ozel G.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, cilt.47, ss.1845-1865, 2018 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 47 Konu: 6
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1080/03610918.2017.1327069
  • Dergi Adı: COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
  • Sayfa Sayıları: ss.1845-1865

Özet

Shrinkage estimator is a commonly applied solution to the general problem caused by multicollinearity. Recently, the ridge regression (RR) estimators for estimating the ridge parameter k in the negative binomial (NB) regression have been proposed. The Jackknifed estimators are obtained to remedy the multicollinearity and reduce the bias. A simulation study is provided to evaluate the performance of estimators. Both mean squared error (MSE) and the percentage relative error (PRE) are considered as the performance criteria. The simulated result indicated that some of proposed Jackknifed estimators should be preferred to the ML method and ridge estimators to reduce MSE and bias.