Interval shrinkage estimation of two-parameter exponential distribution with random censored data

Document Type : Research Paper

Authors

1 Department of Mathematics, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran.

2 Department of Statistics, Payam Noor University, 4697-19395, Tehran, Iran

3 Department of Statistics, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

The use of the two-parameter exponential distribution model in fitting survival and reliability analysis data in the presence of censored random data has recently attracted the attention of a large number of authors. Considering the importance of the model, its parameter estimation is discussed using the method of moment, maximum likelihood and shrinkage estimation. To present the interval shrinkage estimator, it is first proved that the moment estimators are asymptotically unbiased and the interval shrinkage estimator performs better compared to other estimators. Finally, using two real data sets and statistical criteria, the goodness of fit of the model is compared with censored random data based on parameter estimation methods.

Keywords

Main Subjects


[1] Ahmad, SP, & Bhat, BA. (2010). Posterior Estimates of Two-Parameter Exponential Distribution using SPLUS Software. Journal of Reliability and Statistical Studies, 3(2) 27–34.
[2] Baloui, M., Deiri, AE, Hormozinejad, F., & Jamkhane, G. (2021). Efficiency of some shrinking estimators of Pareto-Rayleigh distribution shape parameter. Autumn and Winter Journal of Statistical Sciences, 15(2), 407–427.
[3] Bartholomew, DJ. (1957). A problem in life testing. J. Amer, Statist. Assoc., 52(279), 350–355.
[4] Balakrishnan, N., & Sundhu, RA. (1996). Best Linear Unbiased and Maximum Likelihood Estimation for Exponential Distributions Under General Progressive Type-II Censored Samples. Sankhya: The Indian Journal of Statistics, Series B, Part 1, 58(1), 1–9.
[5] Ban Ghanim, RS., AL-Ani, AL-Rassam, RS., & Rashed, SN. (2020). Bayesian Estimation for Two-Parameter Exponential Distribution Using Linear Transformation of Reliability Function, 8(1), 242–247.
[6] Davis, DJ. (1952). The analysis of some failure data. J. Amer, Statist. Assoc., 47, 133–150.
[7] Epstein, B. (1958). Exponential distribution and its role in life testing. Industrial Quality control., 15, 4–6.
[8] Gilbert, JP. (1962), Random censorship. Ph.D. Thesis, University of Chicago.
[9] Golosnoy, V., & Liesenfeld, R. (2011). Interval shrinkage estimators, J. Appl. Stat., 38, 465–477.
[10] Hussein Ali AL-Hakeema, Jayanthi Arasanb, & Mohd Shafie Bin Mustafab. (2023). Parameter estimation for the generalized exponential distribution in the presence of interval censored data and covariate. Int. J. Nonlinear Anal. Appl., 14(1) 739–751.
[11] Krishna, H., & Goel, N. (2017). Classical and Bayesian Inference in Two Parameter Exponential Distribution with Randomly Censored Data. Computational Statistics, 33, 249–275.
[12] Kourouklis, S. (1994). Estimation in The Two-Parameter Exponential Distribution with Prior Information. IEEE Transactions on reliability, 43, 446–450.
[13] Koziol JA., & Green, SB. (1976). A Cramer-von Mises statistic for random censored data. Biometrika, 63, 465–474.
[14] Lam, BK., Sinha, & Z, Wu. (1994). Estimation of Parameters in a Two-Parameter Exponential Distribution using Ranked Set Sample. Ann. Inst. Statist. Math., 46(4), 723–736.
[15] Nasiri, P. (2022). Interval shrinkage estimation of the parameter of exponential distribution in the presence of outliers under loss functions. Statistics in Transition new series, 23(3), 65–78.
[16] Sohrabi, E., & Jabbari Nooghabi, M. (2024). Estimation of the parameters of Weibull distribution and net premium against outliers. Iranian Journal of Insurance Research, 13(1), 43–60.
[17] Thompson, JR. (1968). Some shrinkage techniques for estimating the mean. Journal of American Statistical Association, 63, 113–122.
[18] Upadhyay, SK., & Singh, U. (2007). Bayes Estimator for Two-Parameter Exponential Distribution. Commun. Statist. Theor. Meth., 24(1), 227–240.