Keywords and phrases: approximate Bayes estimation, Weibull distribution, weighted LINEX loss function, weighted least squared.
Received: October 18, 2021; Accepted: November 17, 2021; Published: December 27, 2021
How to cite this article: Fuad S. Alduais, Approximate Bayes estimation of the Weibull distribution under weighted LINEX loss function, Advances and Applications in Statistics 72 (2022), 25-39. DOI: 10.17654/0972361722002
This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License
References:
[1] F. S. Al-Duais, Bayesian reliability analysis based on the Weibull model under weighted general entropy loss function, Alexandria Engineering Journal 61(1) (2022), 247-255. [2] B. Abbasi, A. H. E. Jahromi, J. Arkat and M. Hosseinkouchack, Estimating the parameters of Weibull distribution using simulated annealing algorithm, Appl. Math. Comput. 183(1) (2006), 85-93. [3] N. Balakrishnan and M. Kateri, On the maximum likelihood estimation of parameters of Weibull distribution based on complete and censored data, Statistics and Probability Letters 78(17) (2008), 2971-2975. [4] M. Teimouri, S. M. Hoseini and S. Nadarajah, Comparison of estimation methods for the Weibull distribution, Statistics 47(1) (2013), 93-109. [5] P. K. Chaurasiya, S. Ahmed and V. Warudkar, Study of different parameters estimation methods of Weibull distribution to determine wind power density using ground based Doppler SODAR instrument, Alexandria Engineering Journal 57(4) (2018), 2299-2311. [6] T. Zhu, Reliability estimation for two-parameter Weibull distribution under block censoring, Reliability Engineering and System Safety 203 (2020), 107071. [7] F. S. Al-Duais, Using non-linear programming to determine weighted coefficient of balanced loss function for estimating parameters and reliability function of Weibull distribution, Adv. Appl. Stat. 62(1) (2020), 31-53. [8] A. Genc, M. Erisoglu, A. Pekgor, G. Oturanc, A. Hepbasli and K. Ulgen, Estimation of wind power potential using Weibull distribution, Energy Sources 27(9) (2005), 809-822. [9] R. L. Smith and J. Naylor, A comparison of maximum likelihood and Bayesian estimators for the three-parameter Weibull distribution, J. Roy. Statist. Soc. Ser. C 36(3) (1987), 358-369. [10] L. F. Zhang, M. Xie and L. C. Tang, A study of two estimation approaches for parameters of Weibull distribution based on WPP, Reliability Engineering and System Safety 92(3) (2007), 360-368. [11] C. B. Guure and N. A. Ibrahim, Bayesian analysis of the survival function and failure rate of Weibull distribution with censored data, Math. Probl. Eng. (2012). [12] C. B. Guure, N. A. Ibrahim, M. B. Adam, S. Bosomprah and A. O. Ahmed, Bayesian parameter and reliability estimate of Weibull failure time distribution, Bull. Malays. Math. Sci. Soc. (2) 37(3) (2014), 611-632. [13] N. R. Mann, R. E. Schafer and N. D. Singpurwalla, Methods for Statistical Analysis of Reliability and Life Data, John Wiley and Sons, New York, 1974. [14] B. Bergman, Estimation of Weibull parameters using a weight function, Journal of Materials Science Letters 5(6) (1986), 611-614. [15] B. N. Pandey, N. Dwividi and B. Pulastya, Comparison between Bayesian and maximum likelihood estimation of the scale parameter in Weibull distribution with known shape under LINEX loss function, Journal of Scientific Research 55 (2011), 163-172. [16] D. V. Lindley, Approximate Bayesian method, Trabajos de Estadistica 31(1) (1980), 223-237. [17] A. Zellner, Bayesian estimation and prediction using asymmetric loss functions, J. Amer. Stat. Assoc. 81 (1986), 446-451. [18] A. P. Basu and N. Ebrahimi, Bayesian approach to life testing and reliability estimation using asymmetric loss-function, J. Statist. Plann. Infer. 29 (1991), 21 31. [19] F. S. Al-Duais, Bayesian analysis of record statistic from the inverse Weibull distribution under balanced loss function, Math. Probl. Eng. (2021). [20] F. S. Al-Duais and M. Y. Hmood, Bayesian and non-Bayesian estimation of the Lomax model based on upper record values under weighted LINEX loss function, Periodicals of Engineering and Natural Sciences (PEN) 8(3) (2020), 1786-1794.
|