Estimating the Parameters of Exponential-Rayleigh Distribution for Progressively Censoring Data with S- Function about COVID-19
Main Article Content
Abstract
The two parameters of Exponential-Rayleigh distribution were estimated using the maximum likelihood estimation method (MLE) for progressively censoring data. To find estimated values for these two scale parameters using real data for COVID-19 which was taken from the Iraqi Ministry of Health and Environment, AL-Karkh General Hospital. Then the Chi-square test was utilized to determine if the sample (data) corresponded with the Exponential-Rayleigh distribution (ER). Employing the nonlinear membership function (s-function) to find fuzzy numbers for these parameters estimators. Then utilizing the ranking function transforms the fuzzy numbers into crisp numbers. Finally, using mean square error (MSE) to compare the outcomes of the survival function before and after fuzzy work. The period of study was (May, June, July, and August). The number of patients who entered the study during the above period was 1058 patients. Six cases have been ruled out including: The number of prisoners was 26. The number of people with negative swabs was 48. The number of patients who exit status was unknown was 29. The number of patients who escaped from the hospital was 2. The number of patients transferred to other hospitals was 35. The number of patients discharged at their responsibility was 133. Then the number of patients who entered the (study) hospital which is the sample size becomes (n=785). The number of patients who died during the period of study was (m=88). The number of patients who survived during the study period was (n-m=697).
Received 16/10/2022,
Revised 03/02/2023,
Accepted 05/02/2023,
Published Online First 20/07/2023
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
Makhdoom I, Nasiri P, Pak A. Estimating the Parameter of Exponential Distribution under Type II Censoring From Fuzzy Data. J Mod Appl Stat Methods. 2016; 15(2): 495–509. https://doi.org/10.22237/jmasm/1478003280
Zadeh LA. Fuzzy sets. Information and control. 1965; 8(3): 338-53.
Abdullah SN, Huessian IH. Estimate the parameters of Weibull distribution by using nonlinear membership function by Gaussian function. J Phys Conf Ser. 2020; 1591(1): 1-8. https://doi.org/10.1088/1742-6596/1591/1/012040
Alkanani IH, Kadhum HJ. Survival estimation for singly type one censored sample based on generalized Rayleigh distribution. Baghdad Sci J. 2014; 11(2): 193–201. https://doi.org/10.21123/bsj.2014.11.2.193-201
Chaturvedi A, Singh SK, Singh U. Statistical inferences of type-II progressively hybrid censored fuzzy data with rayleigh distribution. Austrian J Stat. 2018; 47(3): 40–62. https://doi.org/10.17713/ajs.v47i3.752
Abadi QS, AL-Kanani IH. Progressively censoring data to estimate the survival function in the new mixture distribution. J Crit Rev. 2020; 7(1): 527–30. http://dx.doi.org/10.31838/jcr.07.01.104
Abadi QS, Al-kanani IH. Estimating of Survival Function under Type One Censoring Sample for Mixture Distribution. 2020; 33(4): 102–9. https://doi.org/10.30526/ 33.4.2515
Heidari KF, Deiri E, Jamkhaneh EB. E-Bayesian and Hierarchical Bayesian Estimation of Rayleigh Distribution Parameter with Type-II Censoring from Imprecise Data. J Indian Soc Probab Stat. 2022; 23(1): 63–76. Available from: https://doi.org/10.1007/s41096-021-00112-3
Mazaal AR, Karam NS, Karam GS. Comparing weibull stress - strength reliability Bayesian estimators for singly type II censored data under different loss functions. Baghdad Sci J. 2021; 18(2): 306–14. https://doi.org/10.21123/bsj.2021.18.2.0306
Algarni A, Elgarhy M, M. Almarashi A, Fayomi A, R. El-Saeed A. Classical and Bayesian Estimation of the Inverse Weibull Distribution: Using Progressive Type-I Censoring Scheme. Adv Civ Eng. 2021; 2021. https://doi.org/10.1155/2021/5701529
Yass SB, Kanani IHA. The Estimation of Survival Function for Modified Weibull Extension Distribution Using Two-Symmetric Pentagonal Fuzzy Numbers with Simulation. J Phys Conf Ser. 2022; 2322(1): 1-16. https://doi.org/10.1088/1742-6596/2322/1/012024
Hussein LK, Hussein IH, Rasheed HA. An Estimation of Survival and Hazard Rate Functions of Exponential Rayleigh Distribution. Ibn AL- Haitham J Pure Appl Sci. 2021; 34(4): 93–107. https://doi.org/10.30526/34.4.2706
Nassar M, Alotaibi R, Dey S. Estimation Based on Adaptive Progressively Censored under Competing Risks Model with Engineering Applications. Math Probl Eng. 2022; 2022. https://doi.org/10.1155/2022/6731230
Rosenblad A. The Concise Encyclopedia of Statistics. J Appl Stat . 2011; 38(4): 867–868.
Hameed HH, Al-Saedi HM. The majorant function modelling to solve nonlinear algebraic system. J Interdiscip Math. 2021; 24(7): 1825–9. https://doi.org/10.1080/09720502.2021.1963518
Zimmermann HJ. Fuzzy set theory. WiREs Rev Comput Stat. 2010; 2(3): 317–32.