[RETRACTED] Comparison of Parameter Estimation Methods in Weibull Distribution
Retracted for violating publication ethics – copied content
Abstract
The main objective of this study is to compare the parameter estimation methods for Weibull distribution. We consider maximum likelihood and Bayes estimation methods for the scale and shape parameters of Weibull distribution. While computing the Bayes estimates for a Weibull distribution, the continuous conjugate joint prior distribution of the shape and scale parameters does not exist and the closed form expressions of the Bayes estimators cannot be obtained. In this study, we assume that the scale and shape parameters have the exponential prior and they are independently distributed. We use the Lindley approximation and the Markov Chain Monte Carlo (MCMC) method to obtain the approximate Bayes estimators. In simulation study we compare the effectiveness of the parameter estimation methods with Monte Carlo simulations.
Downloads
References
B. Pandeya, “Estimation of wind energy potential and comparison of six Weibull parameters estimation methods for two potential locations in Nepal,” Int. J. Energy Environ. Eng., vol. 13, no. 3, pp. 955–966, 2022, doi: 10.1007/s40095-021-00444-7.
S. Guo, “A comparison study of three types of parameter estimation methods on weibull model,” Advances in Intelligent Systems and Computing, vol. 1244. pp. 706–711, 2021, doi: 10.1007/978-3-030-53980-1_103.
D. F. Muñoz, “Comparison of five estimation methods for the parameters of the Johnson unbounded distribution using simulated and real-data samples,” Comput. Stat., 2025, doi: 10.1007/s00180-024-01596-w.
A. Hossain and H. Howlader, “Unweighted least squares estimation of weibull parameters,” J. Stat. Comput. Simul., vol. 54, no. 1–3, pp. 265–271, Apr. 1996, doi: 10.1080/00949659608811732.
Ahmed, “Comparison of the Bayesian and Maximum Likelihood Estimation for Weibull Distribution,” J. Math. Stat., vol. 6, no. 2, pp. 100–104, Apr. 2010, doi: 10.3844/jmssp.2010.100.104.
A. Hossain and W. Zimmer, “Comparison of estimation methods for weibull parameters: Complete and censored samples,” J. Stat. Comput. Simul., vol. 73, no. 2, pp. 145–153, Jan. 2003, doi: 10.1080/00949650215730.
D. R. Cox and D. Oakes, “Analysis of Survival Data,” Anal. Surviv. Data, pp. 142–155, 2018, doi: 10.1201/9781315137438-9.
J. F. Lawless, “Statistical Models and Methods for Lifetime Data,” John Wiley Sons, 1982.
R. M. Soland, “Bayesian Analysis of the Weibull Process With Unknown Scale and Shape Parameters,” IEEE Trans. Reliab., vol. R-18, no. 4, pp. 181–184, Nov. 1969, doi: 10.1109/TR.1969.5216348.
C. B. Guure and N. A. Ibrahim, “Methods for Estimating the 2-Parameter Weibull Distribution with Type-I Censored Data,” Res. J. Appl. Sci. Eng. Technol., vol. 5, no. 3, pp. 689–694, Jan. 2013, doi: 10.19026/rjaset.5.5010.
A. Gautam, “Comparison of Weibull parameter estimation methods using LiDAR and mast wind data in an Indian offshore site: The Gulf of Khambhat,” Ocean Eng., vol. 266, 2022, doi: 10.1016/j.oceaneng.2022.112927.
P. H. Jou, “Comparison of parameter estimation methods of the two-parameter Weibull distribution,” Sustain. Water Resour. Manag., vol. 8, no. 4, 2022, doi: 10.1007/s40899-022-00709-x.
M. Sumair, “Efficiency comparison of historical and newly developed Weibull parameters estimation methods,” Energy Explor. Exploit., vol. 39, no. 6, pp. 2257–2278, 2021, doi: 10.1177/0144598720959758.
O. Jalnefjord, “Comparison of methods for intravoxel incoherent motion parameter estimation in the brain from flow-compensated and non-flow-compensated diffusion-encoded data,” Magn. Reson. Med., vol. 92, no. 1, pp. 303–318, 2024, doi: 10.1002/mrm.30042.
E. Uzun, “Comparison of Parameter Estimation Methods for Determining the Parametersof the Battery Electrical Equivalent Circuit Model,” Electrical-Electronics and Biomedical Engineering Conference, ELECO 2024 - Proceedings. 2024, doi: 10.1109/ELECO64362.2024.10847219.
X. Yang, “Comparison of Three-parameter Weibull Shape Parameter Estimation Methods and Its Recommended Values,” Jixie Gongcheng Xuebao/Journal Mech. Eng., vol. 60, no. 16, pp. 367–376, 2024, doi: 10.3901/JME.2024.16.367.
S. Innan, “Parameter-Free Interval Priority Weight Estimation Methods Based on Minimum Conceivable Ranges Under a Crisp Pairwise Comparison Matrix,” J. Adv. Comput. Intell. Intell. Informatics, vol. 28, no. 2, pp. 333–351, 2024, doi: 10.20965/jaciii.2024.p0333.
D. Kundu and D. Mitra, “Bayesian inference of Weibull distribution based on left truncated and right censored data,” Comput. Stat. Data Anal., vol. 99, pp. 38–50, Jul. 2016, doi: 10.1016/j.csda.2016.01.001.
Papadopoulos and Tsokos, “Bayesian analysis of the Weibull failure model with unknown scale and shape parameters,” Statistica.
P. Nizeyimana, “Bayesian Estimation of Neyman–Scott Rectangular Pulse Model Parameters in Comparison with Other Parameter Estimation Methods,” Water (Switzerland), vol. 16, no. 17, 2024, doi: 10.3390/w16172515.
Copyright (c) 2025 Indonesian Journal of Data and Science
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
License and Copyright Agreement
By submitting a manuscript to the Indonesian Journal of Data and Science (IJODAS), the author(s) confirm and agree to the following:
- All co-authors have given their consent to enter into this agreement.
- The submitted manuscript has not been formally published elsewhere, except as an abstract, thesis, or in the context of a lecture, review, or overlay journal.
- The manuscript is not currently under review or consideration by another journal or publisher.
- All authors have approved the manuscript and its submission to IJODAS, and where applicable, have received institutional approval (tacit or explicit) from affiliated organizations.
- The authors have secured appropriate permissions to reproduce any third-party material included in the manuscript that may be under copyright.
- The authors agree to abide by the licensing and copyright terms outlined below.
Copyright Policy
Authors who publish in IJODAS retain the copyright to their work and grant the journal the right of first publication. The published work is simultaneously licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0) , which permits others to share and adapt the work for non-commercial purposes, with proper attribution to the authors and the initial publication in this journal.
Reuse and Distribution
- Authors may enter into separate, additional contractual arrangements for non-exclusive distribution of the journal-published version of the article (e.g., institutional repositories, book chapters), provided there is proper acknowledgment of its initial publication in IJODAS.
- Prior to and during the submission process, we encourage authors to archive preprints and accepted versions of their work on personal websites or institutional repositories. This method supports scholarly communication, visibility, and early citation.
For more details on the terms of the Creative Commons license used by IJODAS, please visit the official license page.
