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Journals / Sigma Mühendislik ve Fen Bilimleri Dergisi / 2020 / Cilt: 38 - Sayı: 3

COMPARISON OF PARAMETER ESTIMATION METHODS IN WEIBULL DISTRIBUTION

Esin KÖKSAL BABACAN
Samet KAYA
Pages
1609–1621
Publication Date
2021-10-05
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In simulation study we compare the effectiveness of the parameter estimation methods with Monte Carlo simulations.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The main objective of this study is to compare the parameter estimation methods for Weibull distribution.
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Key Findings & Data:We consider maximum likelihood and Bayes estimation methods for the scale and shape parameters of Weibull distribution.
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Conclusion & Impact:In simulation study we compare the effectiveness of the parameter estimation methods with Monte Carlo simulations.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Weibull distribution · bayes estimator · lindley approximation

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.

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Cite This Article

BABACAN, E. K., KAYA, S. (2021). COMPARISON OF PARAMETER ESTIMATION METHODS IN WEIBULL DISTRIBUTION. Sigma Mühendislik ve Fen Bilimleri Dergisi, Cilt: 38 - Sayı: 3, 1609–1621. https://ajindex.com/articles/419094

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References (15)

  1. Hossain. A. And Howlader, H. A. (1996). Unweighted least squares estimation of Weibull parameters. Journal of Statistical Computation and Simulation, 54, 265-271
  2. Ahmed, Al O. M., Al-Kutubi, H. S. and Ibrahim, N. A. (2010). Comparison of the Bayesian and Maximum Likelihood Estimation for Weibull Distribution, Journal of Mathematics and Statistics 6 (2): 100-104
  3. Hossain, M. A and Zimmer, W. J. (2003). Comparison of Estimation Methods for Weibull Parameters: Complete and Censored Samples, Journal of Statistical Computation and Simulation, Vol. 73(2), pp. 145-153
  4. Cox, D. R. And Oakes, D. (1984). Analysis of Survival Data. Chapman and Hall, London
  5. Lawless, J. F. (1982). Statistical Models and Methods for Life Time Data. John Wiley and Sons, New York
  6. Soland, R. M, (1969). Bayesian Analysis Of The Weibull Process With Unknown Scale And Shape Parameters, IEEE Transactions On Reliability, Vol. R- 18 No. 4, 181-184
  7. Guure, C. B. and Ibrahim, N. A.(2013). Methods for Estimating the 2-Parameter Weibull Distribution with Type-I Censored Data, Research Journal of Applied Sciences, Engineering and Technology 5(3): 689-694
  8. Kundu, D., Mitr, D. (2016). Bayesian Inference of Weibull Distribution Based on Left Truncated and Right Censored Data, Computational Statistics & Data Analysis, Volume 99, July 2016, Pages 38-50
  9. Tsokos, C. P. (1972), “Bayesian approach to reliability: theory and simulation”, in Ann. Reliab. and Maintain. Symp., San Francisco, pp. 78-87 [10] Papadopoulos, A. S. and Tsokos, C.P. (1976). Bayesian analysis of the Weibull failure model with unknown scale and shape parameters, Statistica, XXXVI, NO. 4, Oct.-Dec
  10. Banerjee, A. and Kundu, D. (2008). Inference Based on Type-II Hybrid Censored Data From a Weibull Distribution, IEEE Trans. Reliab., Vol
  11. , pp. 369-378, Ju
  12. Babacan, E. K., Kaya, S. (2019). A Simulation Study of the Bayes Estimator for Parameters in Weibull Distribution. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat., Volume 68, Number 2, Pages 1664.1674 (2019), Doi: 10.31801/cfsuasmas.455276
  13. Kundu, D. (2008). Bayesian Inference and Life Testing Plan for the Weibull Distribution in Presence of Progressive Censoring, Technometrics 50 (2) pp. 114-154, May
  14. Lindley, D. (1980). Approximate Bayes Methods, University College London
  15. Singhl, R., Singh, S. K., Singh, U., Singh, G.P. (2008). Bayes estimator of generalized-exponential Parameters Under Linex Loss Function Using Lind