A Jackknifed estimators for the negative binomial regression model

TÜRKAN S., Ozel G.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, vol.47, no.6, pp.1845-1865, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 47 Issue: 6
  • Publication Date: 2018
  • Doi Number: 10.1080/03610918.2017.1327069
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1845-1865
  • Keywords: Jackknifed estimators, Maximum likelihood, MSE, Negative binomial regression, Ridge regression, Simulation, RIDGE-REGRESSION, PARAMETERS
  • Hacettepe University Affiliated: Yes


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.