A weighted error-minimizer parameter estimation technique for one-inflated positive Poisson distribution
An error-minimizing estimator is always preferred in model fittings. However, each error-minimizing estimator minimizes error differently. This paper combines four error-minimizing estimators, which are root mean-squared error, mean absolute error, root mean-squared log error and mean absolute perce...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Results in Control and Optimization |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666720725000554 |
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| Summary: | An error-minimizing estimator is always preferred in model fittings. However, each error-minimizing estimator minimizes error differently. This paper combines four error-minimizing estimators, which are root mean-squared error, mean absolute error, root mean-squared log error and mean absolute percentage error via a weighted approach. The estimation involves two levels. In the first-level estimation, the estimated parameters are obtained by minimizing error values differently and separately. In the second-level estimation, the resulting estimates from the first-level estimation are combined by either fixed and controlled weights or free and uncontrolled weights. A real crime dataset on the frequency of drunk drivers was considered for demonstration of the technique. |
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| ISSN: | 2666-7207 |