Improved two-parameter estimators for the negative binomial and Poisson regression models

Cetinkaya M. K., KAÇIRANLAR S.

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, cilt.89, sa.14, ss.2645-2660, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 89 Sayı: 14
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1080/00949655.2019.1628235
  • Dergi Adı: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2645-2660
  • Anahtar Kelimeler: Liu estimator, maximum likelihood estimator, negative binomial regression model, Poisson regression model, ridge estimator, two-parameter estimator, RIDGE-REGRESSION, PARAMETERS
  • Çukurova Üniversitesi Adresli: Evet

Özet

Negative binomial regression (NBR) and Poisson regression (PR) applications have become very popular in the analysis of count data in recent years. However, if there is a high degree of relationship between the independent variables, the problem of multicollinearity arises in these models. We introduce new two-parameter estimators (TPEs) for the NBR and the PR models by unifying the two-parameter estimator (TPE) of ozkale and Kaciranlar [The restricted and unrestricted two-parameter estimators. Commun Stat Theory Methods. 2007;36:2707-2725]. These new estimators are general estimators which include maximum likelihood (ML) estimator, ridge estimator (RE), Liu estimator (LE) and contraction estimator (CE) as special cases. Furthermore, biasing parameters of these estimators are given and a Monte Carlo simulation is done to evaluate the performance of these estimators using mean square error (MSE) criterion. The benefits of the new TPEs are also illustrated in an empirical application. The results show that the new proposed TPEs for the NBR and the PR models are better than the ML estimator, the RE and the LE.