Estimation of the stress–strength reliability using lower record ranked set sampling scheme under the generalized exponential distribution

2019 ◽  
Vol 90 (1) ◽  
pp. 51-74
Author(s):  
Amineh Sadeghpour ◽  
Mahdi Salehi ◽  
Ahmad Nezakati
Keyword(s):  
Exponential Distribution ◽  
Ranked Set Sampling ◽  
Sampling Scheme ◽  
Generalized Exponential Distribution ◽  
Lower Record

scholarly journals Bayesian Prediction of Order Statistics Based on k-Record Values from a Generalized Exponential Distribution

Stats ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 447-456
Author(s):  
Zoran Vidović
Keyword(s):  
Order Statistics ◽  
Exponential Distribution ◽  
Record Values ◽  
Bayesian Prediction ◽  
Generalized Exponential Distribution ◽  
Lower Record ◽  
Lower Record Values ◽  
Future Sample

We examine in this paper the implementation of Bayesian point predictors of order statistics from a future sample based on the k th lower record values from a generalized exponential distribution.

Estimation of Stress–Strength Reliability Based on Ranked Set Sampling for Generalized Exponential Distribution

2020 ◽  
pp. 2150011
Author(s):  
Melek Esemen ◽  
Selma Gurler ◽  
Busra Sevinc
Keyword(s):  
Exponential Distribution ◽  
Bayesian Methods ◽  
Sampling Methods ◽  
Real Data ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Generalized Exponential Distribution ◽  
R Software ◽  
Estimate Reliability ◽  
Approximate Bayesian

In this paper, we consider the estimation of the reliability in a stress–strength model by the maximum likelihood and Bayesian methods under generalized exponential distribution. We provide the estimation of the reliability with simple random sampling and ranked set sampling methods. Lindley’s algorithm is used to obtain the approximate Bayesian estimation of the reliability with gamma priors. The results are compared in terms of relative efficiency in different sample sizes through a simulation study with R-software. Finally, two real data examples are presented to estimate reliability.

scholarly journals Generalized Exponential Distribution - Estimation of parameters using modifications in methods of Ranked Set Sampling

2021 ◽  
Author(s):  
Vyomesh Prahlad Nandurbarkar ◽  
Ashok Shanubhogue
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Exponential Distribution ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Likelihood Method ◽  
Generalized Exponential Distribution ◽  
Data Set ◽  
Extreme Ranked Set Sampling

Abstract In this study, we estimate the parameters of the Generalized Exponential Distribution using Moving Extreme Ranked Set Sampling (MERSS). Using the maximum likelihood estimation method, we derive the expressions. MERSS estimates are compared with estimates obtained by simple random sampling (SRS) using a real data set. We also study the other variations of the methods of Ranked Set Sampling like Quartile Ranked Set Sampling(QRSS), Median Ranked Set Sampling(MRSS) and Flexible Ranked Set Sampling(FLERSS) (a scheme based on QRSS and MRSS). For known shape parameter values, we present coefficients for linear combinations of order statistics for least squares estimates. Here, the expressions are derived through maximum likelihood, and the estimates are calculated numerically. Simulated results indicate that estimates generated using least-squares and the maximum likelihood method for Ranked Set Sampling (RSS) perform better than those generated using Simple Random Sampling (SRS). Asymptotically, MERSS outperforms SRS, QRSS, MRSS, and FLERSS.

scholarly journals Ranked set sampling with application of modified Kies exponential distribution

2021 ◽  
Vol 60 (4) ◽  
pp. 4041-4046
Author(s):  
Hassan M. Aljohani ◽  
Ehab M. Almetwally ◽  
Abdulaziz S. Alghamdi ◽  
E.H. Hafez
Keyword(s):  
Exponential Distribution ◽  
Ranked Set Sampling
Sign in / Sign up

Export Citation Format

Share Document