Volume 24 , Issue 2 , PP: 19-29, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Zainab A. Aldraji 1 * , Rehab Noori shalan 2
Doi: https://doi.org/10.54216/IJNS.240202
In this paper, maximum likelihood estimation method (MLEM), one of the most well-liked and frequently applied classic methods, is used to estimate the two scale and one shape parameters of the Generalized Exponential-Rayleigh distribution for type-I censored data, which is one of the most Rights censored data. Based on an iterative process to get approximated values for these two scale parameters and one shape parameter using the Newton-Raphson method to locate estimate value for these parameters by using the simulation procedure utilizing monte-Carlo technique to find Reliability function underneath various sample sizes and the initial values are different for the parameters for all estimated parameters of Generalized Exponential-Rayleigh by implement the initial value in the MATLAP program, Subsequently, conducting a comparative analysis between the estimated reliability function and its non-estimated counterpart employing the mean squares error methodology. In the last finding the pdf function f (t), reliability function R (t) and hazard function h (t) for simulation data. Also, we provide some examples to clarify how can we apply our results on fuzzy data tables
Generalized Exponential Rayleigh distribution , censored sample , Reliability function , Simulation, fuzzy data.
[1] I. H. Hussein, “Estimating of Survival Function under Type One Censoring Sample for Mixture Distribution Estimating of Survival Function under Type One Censoring Sample for Mixture Distribution,” no. April, 2021.
[2] I. H. Hussein, “STATISTICAL PROPERTIES OF GENERALIZEDEXPONENTIAL RAYLEIGH STATISTICAL PROPERTIES OF GENERALIZED,” no. July, 2023.
[3] B. S. Journal, R. N. Shatti, and I. H. Al-kinani, “Estimating the Parameters of Exponential-Rayleigh Distribution under Type-I Censored Data Abstract :,” 2023.
[4] A. T. Mohammed and I. H. Hussein, “Nonparametric estimation for hazard rate function by wavelet procedures with simulation,” IOP Conference Series: Materials Science and Engineering, vol. 571, no. 1, 2019, doi: 10.1088/1757-899X/571/1/012013.
[5] A. T. Mohammed and I. Hussein, “Density estimation for right censored data using hybrid breslow and semi-symmetric wavelet,” Journal of Engineering and Applied Sciences, vol. 14, no. 1, pp. 242–246, 2019, doi: 10.3923/jeasci.2019.242.246.
[6] R. N. Shalan and I. H. Alkanani, “Some Methods to Estimate the Parameters of Generalized Exponential Rayleigh Model by Simulation,” 2023.
[7] I. H. Hussein, “STATISTICAL PROPERTIES OF GENERALIZEDEXPONENTIAL RAYLEIGH STATISTICAL PROPERTIES OF GENERALIZED,” no. July, 2023.
[8] R. N. Shalan and I. H. Alkanani, “The Simulation Technique to Estimate the Parameters of Generalized Exponential Rayleigh Model,” Iraqi Journal of Science, 2024.
[9] R. Y. Rubinstein and D. P. Kroese, Simulation and the Monte Carlo method. John Wiley & Sons, 2016.
[10] Maissam Jdid, Khalifa Alshaqsi. (2023). Optimal Value of the Service Rate in the Unlimited Model M\M\1. Journal of Neutrosophic and Fuzzy Systems, 6 ( 1 ), 08-13 (Doi : https://doi.org/10.54216/JNFS.060101).
[11] Kausik Das, Sahidul Islam. (2023). A Multi Objective Epq Model with Uniform Demand and Productıon Rate Along Wıth Shortages Under Intuitioistic Fuzzy Programmıng Approach. Journal of Neutrosophic and Fuzzy Systems, 6 ( 1 ), 39-51 (Doi : https://doi.org/10.54216/JNFS.060105).
[12] Maissam Jdid, A. Salama. (2023). Using the Inverse Transformation Method to Generate Random Variables that follow the Neutrosophic Uniform Probability Distribution. Journal of Neutrosophic and Fuzzy Systems, 6 ( 2 ), 15-22 (Doi : https://doi.org/10.54216/JNFS.060202).
[13] R. Sivasamy, M. Mohammed Jabarulla, Broumi said. (2023). Applications of Interval-Valued Pythagorean Fuzzy Soft Graph in Bipolar Fuzzy Frame Works. Journal of Neutrosophic and Fuzzy Systems, 7 ( 2 ), 38-45 (Doi : https://doi.org/10.54216/JNFS.070204)
