E-Bayesian inference for xgamma distribution under progressive type II censoring with binomial removals and their applications

References

• Sen S, Maiti SS, Chandra N. The xgamma distribution: statistical properties and application. Journal of Modern Applied Statistical Methods. 2016;15(1):38
   Google Scholar
• Altun E, Hamedani G. The log-xgamma distribution with inference and application. Journal de la Société Française de Statistique. 2018;159(3):40–55.
   Google Scholar
• Han M. The structure of hierarchical prior distribution and its applications. Chinese Operations Research and Management Science. 1997;6(3):31–40.
   Google Scholar
• Han M. E-Bayesian estimations of the reliability and its E-posterior risk under different loss functions. Communications in Statistics-Simulation and Computation. 2020;9(6):1527–1545.
   Google Scholar
• Han M. E-Bayesian estimation and its E-MSE under the scaled squared error loss function, for exponential distribution as example. Communications in Statistics - Simulation and Computation. 2019;48(6):1880–1890.
   Web of Science ®Google Scholar
• Han M. E-Bayesian estimation of the Exponentiated distribution family parameter under LINEX loss function. Communications in Statistics - Theory and Methods. 2019;48(3):648–659.
   Web of Science ®Google Scholar
• Han M. E-Bayesian estimations of parameter and its evaluation standard: e-MSE (expected mean square error) under different loss functions. Communnication in Statistics - Simulation and Computation. 2019;50(7):1971–1988.
   Web of Science ®Google Scholar
• Han M. E-Bayesian estimation and its E-posterior risk of the exponential distribution parameter based on complete and type I censored samples. Communications in Statistics - Theory and Methods. 2020;49(8):1858–1872.
   Web of Science ®Google Scholar
• Balakrishnan N, Sandhu RA. A simple simulational algorithm for generating progressive type-II censored samples. Am Stat. 1995;49(2):229–230.
   Web of Science ®Google Scholar
• Balakrishnan N, Aggarwala R. Progressive Censoring: theory, Methods, and Applications. Birkhäuser Boston, MA: Springer Science & Business Media; 2000.
   Google Scholar
• K TYCS, Yuen HK, Yuen H-K. Statistical analysis of Weibull distributed lifetime data under Type II progressive censoring with Binomial removals. J Appl Stat. 2000;27(8):1033–1043.
   Web of Science ®Google Scholar
• Wu SJ, Chang CT. Parameter estimations based on Exponential progressive type II censored data with Binomial removals. Int J Inf Manage Sci. 2002;13(3):37–46.
   Google Scholar
• Jaheen ZF, Okasha HM. E-Bayesian estimation for the Burr type-XII model based on type-II censoring. Appl Math Modell. 2011;35(10):4730–4737.
   Web of Science ®Google Scholar
• Okasha HM. E-Bayesian estimation for the Lomax distribution based on type-II censored data. J Egypt Math Soc. 2014;22(3):489–495.
   Google Scholar
• Reyad HM, Ahmed SO. Bayesian and E-Bayesian estimation for the Kumaraswamy distribution based on type-II censoring. International Journal of Advanced Mathematical Sciences. 2016;4(1):10–17
   Google Scholar
• El-Sagheer RM. E-Bayesian estimation for Rayleigh model using progressive type-II censoring data. J Statistical Theory Appl. 2017;16(2):239–247.
   Web of Science ®Google Scholar
• Kızılaslan F. The E-Bayesian and hierarchical Bayesian estimations for the proportional reversed hazard rate model based on record values. J Stat Comput Simul. 2017;87(11):2253–2273.
   Web of Science ®Google Scholar
• Sen S, Chandra N, Maiti SS. Survival estimation in xgamma distribution under progressively type-II right censored scheme. Model Assisted Statistics and Applications. 2018;13(2):107–121
   Google Scholar
• Cohen AC. Progressively Censored Samples in Life Testing. Technometrics. 1963;5(3):327–339.
   Web of Science ®Google Scholar
• Kamps U, Cramer E. On distributions of generalized order statistics. Statistics. 2001;35(3):269–280.
   Web of Science ®Google Scholar
• Tibshirani RJ, Efron B. An Introduction to the Bootstrap. Monographs on statistics and applied probability. 1993;57:1–436.
   Google Scholar
• Calabria R, Pulcini G. On the maximum likelihood and least-squares estimation in the Inverse Weibull distributions. Statistica Application. 1990;2(1):53–66.
   Google Scholar
• Varian HR. A Bayesian Approach to Real Estate Assessment. Studies in Bayesian Econometrics and Statistics: In Honor of L. J. Savage, North-Holland Pub. Co., Amsterdam. 1975;36(1):195–208.
   Google Scholar
• Zellner A. Bayesian estimation and prediction using asymmetric loss functions. J Am Stat Assoc. 1986;81(394):446–451.
   Web of Science ®Google Scholar
• Zellner A. Statistical decision theory and related topics V. New York, NY: Springer; 1994. Bayesian and non-Bayesian estimation using balanced loss functions; p. 377–390.
   Google Scholar
• Berger JO. Statistical decision theory and Bayesian analysis. Springer-Verlag New York, NY: Springer Science & Business Media; 2013.
   Google Scholar
• Gelman A, Carlin JB, Stern HS. Bayesian data analysis. New York: Chapman and Hall/CRC; 2013.
   Google Scholar
• Cox DR, Oakes D. Analysis of survival data. Vol. 21. New York: Chapman & Hall CRC Press; 1984.
   Google Scholar
• Lawless JF. Statistical models and methods for lifetime data. Vol. 362. New Jersey: John Wiley & Sons; 2011.
   Google Scholar
• Thoman DR, Bain LJ. Two sample tests in the Weibull distribution. Technometrics. 1969;11(4):805–815.
   Web of Science ®Google Scholar
• Akaike H. A Bayesian analysis of the minimum AIC procedure. Ann Inst Stat Math. 1978;30(1):9–14.
   Web of Science ®Google Scholar
• Schwarz G. Estimating the Dimension of a Model. Annals of Statistics. 1978;6(2):461–464.
   Web of Science ®Google Scholar