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Bayesian inference for the inverse Weibull distribution based on symmetric and asymmetric balanced loss functions with application
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Marg Higher Institute for Engineering and Modern Technology, Cairo 11721, Egypt, Egypt
Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia, Saudi Arabia
Department of Computing, University of Eastern Finland, FI-70211, Finland, Finland
Submission date: 2023-12-02
Final revision date: 2024-01-22
Acceptance date: 2024-04-14
Online publication date: 2024-04-14
Publication date: 2024-04-14
Corresponding author
Mustafa Mohamed Hasaballah   

Marg Higher Institute for Engineering and Modern Technology, Cairo 11721, Egypt, Egypt
  • Statistical inference methods.
  • Unified hybrid censoring scheme.
  • Maximum Likelihood estimation.
  • Bayesian estimation.
In this study, the unified hybrid censored approach is employed to estimate the parameters of the inverse Weibull distribution, as well as the survival and hazard rate functions. Parameter estimates are obtained using both Bayesian and Maximum Likelihood approaches, with Bayesian estimates acquired through Lindley's approximation method using three distinct balanced loss functions. These encompass both symmetric and asymmetric balanced loss functions, specifically the balanced squared error (BSE) loss function, the balanced linear exponential (BLINEX) loss function, and the balanced general entropy (BGE) loss function. We conduct a simulation study to compare the effectiveness of various estimators, and a real-world data analysis is presented to illustrate practical implementation. Ultimately, our findings indicate that Bayesian parameter estimates consistently outperform their Maximum Likelihood counterparts across all methods.
This research project was supported by the Researchers Supporting Project Number (RSP2024R488), King Saud University, Riyadh, Saudi Arabia.
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