TY  - JOUR
T1  - Two parameter Ridge estimator for the Bell regression model
AU  - Bulut, Y. Murat
AU  - Işılar, Melike
PY  - 2024
DA  - September
Y2  - 2024
DO  - 10.31801/cfsuasmas.1397263
JF  - Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics
JO  - Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat.
PB  - Ankara University
WT  - DergiPark
SN  - 1303-5991
SP  - 712
EP  - 723
VL  - 73
IS  - 3
LA  - en
AB  - One solution to the multicollinearity problem in the Bell regression model, which is utilized for over-dispersion issues, is biased estimators. In recent years, some biased estimators have been proposed in the Bell regression model that can be used in modelling correlated count data. In this article, Bell two-parameter ridge estimator (BTPRE) is proposed. This two-parameter estimator has some advantages over the previously proposed estimators. More efficient results are obtained than the Maximum Likelihood estimator (MLE) and Bell Ridge estimator (BRE) in the case of multicollinearity by using BTPRE. Monte Carlo simulation study and real data results are obtained to show that the proposed estimator is better. Estimators have been compared according to the Mean Squared Error (MSE) criterion. BTPRE is superior to other estimators.
KW  - Regression
KW  - count data
KW  - overdispersion
KW  - multicollinearity
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UR  - https://doi.org/10.31801/cfsuasmas.1397263
L1  - https://dergipark.org.tr/en/download/article-file/3565139
ER  -