| Autor: |
ADEGOKE, TAIWO M., ABIMBOLA, LATIFAT A., OLADOJA, OLADAPO M., OYEBANJO, OYINDAMOLA. R., OBISESAN, K. O. |
| Předmět: |
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| Zdroj: |
Reliability: Theory & Applications; Sep2024, Vol. 19 Issue 3, p744-756, 13p |
| Abstrakt: |
In this study, we derive Bayes' estimators for the unknown parameters of the Inverse Gompertz Distribution (IGD) using three alternative loss functions: the Squared Error Loss Function (SELF), the Entropy Loss Function (ELF), and the Linex Loss Function. Closed-form formulas for Bayes estimators are not possible when both parameters are unknown, hence Lindley's approximation (L-Approximation) is used for computation. We examine the performance of these estimators using their simulated hazards and assess their effectiveness in parameter estimation. It was discovered that as the sample size increases, parameter estimations became more precise and accurate across all functions. However, ELF consistently has lower MSE values than SELF and LINEX, indicating better parameter estimation. This pattern was also seen in the estimation of the hazard function, where ELF regularly beat SELF and LINEX, implying more efficient parameter estimation overall. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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