Journal
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
Volume 52, Issue 3, Pages 854-867Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/03610918.2020.1870694
Keywords
Bell distribution; Bell ridge regression; Maximum likelihood estimator; Mean squared error; Monte Carlo simulation; Multicollinearity
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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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