TY - EJOU
AU - Alshenawy, R.
AU - Sabry, Mohamed A. H.
AU - Almetwally, Ehab M.
AU - Elomngy, Hisham M.
TI - Product Spacing of Stress–Strength under Progressive Hybrid Censored for Exponentiated-Gumbel Distribution
T2 - Computers, Materials \& Continua
PY - 2021
VL - 66
IS - 3
SN - 1546-2226
AB - Maximum product spacing for stress–strength model based on progressive Type-II hybrid censored samples with different cases has been obtained. This paper deals with estimation of the stress strength reliability model *R = P(Y < X)* when the stress and strength are two independent exponentiated Gumbel distribution random variables with different shape parameters but having the same scale parameter. The stress–strength reliability model is estimated under progressive Type-II hybrid censoring samples. Two progressive Type-II hybrid censoring schemes were used, Case I: A sample size of stress is the equal sample size of strength, and same time of hybrid censoring, the product of spacing function under progressive Type-II hybrid censoring schemes. Case II: The sample size of stress is a different sample size of strength, in which the life-testing experiment with a progressive censoring scheme is terminated at a random time *T* ∈ (0,∞). The maximum likelihood estimation and maximum product spacing estimation methods under progressive Type-II hybrid censored samples for the stress strength model have been discussed. A comparison study with classical methods as the maximum likelihood estimation method is discussed. Furthermore, to compare the performance of various cases, Markov chain Monte Carlo simulation is conducted by using iterative procedures as Newton Raphson or conjugate-gradient procedures. Finally, two real datasets are analyzed for illustrative purposes, first data for the breaking strengths of jute fiber, and the second data for the waiting times before the service of the customers of two banks.
KW - Exponentiated Gumbel distribution; stress–strength model; progressive Type-II hybrid censoring; maximum product spacing; maximum likelihood
DO - 10.32604/cmc.2021.014289