On the estimation of Bell regression model using ridge estimator

The bell regression is used, when the response variable is in the form of counts with over dispersion. The bell regression coefficients are generally estimated using the maximum likelihood estimator (MLE). It is known that the performance of the traditional MLE is very sensitive to multicollinearity...

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Bibliographic Details
Published inCommunications in statistics. Simulation and computation Vol. 52; no. 3; pp. 854 - 867
Main Authors Amin, Muhammad, Akram, Muhammad Nauman, Majid, Abdul
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 04.03.2023
Taylor & Francis Ltd
Subjects
Bell distribution
Bell ridge regression
Bells
Maximum likelihood estimator
Maximum likelihood estimators
Mean squared error
Monte Carlo simulation
Multicollinearity
Regression coefficients
Regression models
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Summary:The bell regression is used, when the response variable is in the form of counts with over dispersion. The bell regression coefficients are generally estimated using the maximum likelihood estimator (MLE). It is known that the performance of the traditional MLE is very sensitive to multicollinearity. Therefore, we propose a Bell ridge regression (BRR) as a solution to the multicollinearity problems. For the assessment of BRR, we conduct a Monte Carlo simulation study to monitor the performance of the proposed estimator where the mean squared error (MSE) is considered as an evaluation criterion. Also, two real examples are included to show the superiority of the BRR estimator.
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ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2020.1870694