Hanieh Panahi

The rebounded nano-droplet (RND) is one of the important issues in creating defective coatings. The RND depends on many factors such as angle of droplet impact and velocity. Reliability model can be considered to determine the probability of surface defect under two impact angles in the RND data. In this paper, we have delved into the application of reliability and order-restricted (ORe) estimation methods for two Kumaraswamy populations in the RND data. The study kicks off by evaluating maximum likelihood and Bayesian estimates of the reliability parameter and order-restricted parameters in presence of balanced joint progressive censored data. Bayes estimations for reliability and ORe parameters under two different loss functions are investigated with the Metropolis-Hastings technique and Lindley’s approach. To evaluate order-restricted Bayesian estimates, ordered Beta-Gamma prior is considered. The asymptotic, Bootstrapping and Bayesian bounds are also computed. A thorough simulation analysis is demonstrated to assess the performance of the supplied approaches across various sample sizes. The usefulness of the techniques is illustrated using RND data to prove their versatility in practical applications.

The statistical inference under competing risks model is of great significance in reliability analysis and it is more practical to assume that they have dependent competing causes of failure in actual situations. In this article, we make inference for unknown parameters of a Marshall-Olkin bivariate Kumaraswamy distribution under adaptive progressive hybrid censoring mechanism. The maximum likelihood estimations of the unknown parameters are derived, and the Fisher information matrix is then employed to construct asymptotic confidence intervals. Bayes estimates are evaluated against squared error and linex loss functions assuming ordered Gamma-Dirichlet and Gamma-Dirichlet prior distributions for order restriction and without order restriction cases respectively. The Metropolis-Hasting and Lindley techniques are applied to acquire the estimates of all unknown parameters. A thorough simulation analysis is demonstrated to assess the performance of the supplied approaches across various sample sizes. The usefulness of the techniques is illustrated using real engineering data to prove their versatility in practical applications.

Censoring commonly occurs in real-world scenarios, either intentionally or unintentionally. In addition, in engineering and applied studies, two or more populations are required to perform the experiment. In this article, we develop the order-restricted ( OR ) statistical inference of two inverted exponentiated Rayleigh ( IERayleigh ) populations based on the joint type-II progressive censoring scheme ( J-IIProgCS ). We also consider the problem of the stress-strength reliability Re = P ( X > Y ) estimation under J-IIProgCS. The process of estimating the parameters with order restriction ( PaR-OR ) and Re are performed using the maximum likelihood (ML) and Bayesian methods. On the presumption that the gamma priors are independent, the Bayesian estimate of reliability model is obtained using the hybrid Gibbs within Metropolis-Hastings ( G-MeHa ) algorithm. Moreover, with assumption the ordered Beta-Gamma prior for the shape parameters and the Gamma prior for the common scale parameter, the Bayes estimates of the parameters with order restriction are evaluated. The classical confidence intervals and the Bayesian credible interval are created. A detailed numerical illustration using simulated data sets is carried out. A real engineering example related to splashing phenomenon is considered for illustrative purposes.

The balanced joint progressive censoring scheme gives guidelines for researchers in reducing the expense and period of the experiment with modified efficiency. In this paper, the stress-strength reliability model are considered and investigated, in which two independent samples are generated from Burr Type XII distributions under balanced joint progressive censoring scheme. In the classical analysis, the maximum likelihood estimate of the reliability parameter is derived, then the relevant confidence interval is provided based on the Fisher matrix. Under the Bayesian framework, the Lindley’s technique and hybrid Gibbs within Metropolis-Hastings algorithm are applied to acquire the estimates of the parameters, and the corresponding highest posterior density credible interval of the reliability model is also constructed. The reliability inference is realized by numerical simulation, and the validity of the inference method is analyzed based on the splashing data of silicone oil droplets impact (SSODI) under two surfaces wettability conditions.

The stress-strength reliability (SSRe) model is widely investigated in reliability engineering to determine the probability of the strength component overcomes the stress imposed on it. In this paper, we studied the estimation of SSRe model based on the Burr III distribution under the improved adaptive progressive type-II censoring scheme (IAPrgCS-II). Estimation methods of the SSRe parameters are developed using frequentist and Bayesian approaches. The point and interval estimations using the maximum likelihood are considered to estimate the parameters. Two approximations are applied to compute the Bayes estimates. A simulation study is conducted for the comparison of the methods of estimation. Also, parallel to the development of reliability studies, it is necessary to study its application in different sciences such as engineering. Therefore, the droplet splashing (DrS) data under two wettabilities are proposed as an application of the considered SSRe model and methods. The results show us that the reliability model can be used to amend the quality of coatings.

Censoring is a common occurrence in reliability engineering tests. This article considers estimation of the model parameters and the reliability characteristics of the gamma-mixed Rayleigh distribution based on a novel unified progressive hybrid censoring scheme (UPrgHyCS), where experimenters are allowed more flexibility in designing the test and higher efficiency. The maximum likelihood estimates of the model parameters and reliability are provided using the stochastic expectation–maximization algorithm based on the UPrgHyCS. Further, the Bayesian inference associated with any parametric function of the model parameters is considered using the Markov chain Monte Carlo method with the Metropolis-Hastings (M-H) algorithm. Asymptotic confidence and credible intervals of the proposed quantities are also created. The maximum a posteriori estimates of the model parameters are acquired. Due to the importance of determining the optimal censoring scheme for reliability problems, different optimality criteria are proposed and derived to find it. This method can help to design experiments and get more information about unknown parameters for a given sample size. Finally, comprehensive simulation experiments are provided to investigate the performances of the considered estimates, and a real dataset is analyzed to elucidate the practical application and the optimality criterion work in real life scenarios. The Bayes estimates using the M-H technique show the best performance in terms of error values.

Purpose: The study based on the estimation of the stress–strength reliability parameter plays a vital role in showing system efficiency. In this paper, considering independent strength and stress random variables distributed as inverted exponentiated Rayleigh model, the author have developed estimation procedures for the stress–strength reliability parameter R = P(X>Y) under Type II hybrid censored samples.Design/methodology/approach: The maximum likelihood and Bayesian estimates of R based on Type II hybrid censored samples are evaluated. Because there is no closed form for the Bayes estimate, the author use the Metropolis–Hastings algorithm to obtain approximate Bayes estimate of the reliability parameter. Furthermore, the author construct the asymptotic confidence interval, bootstrap confidence interval and highest posterior density (HPD) credible interval for R. The Monte Carlo simulation study has been conducted to compare the performance of various proposed point and interval estimators. Finally, the validity of the stress–strength reliability model is demonstrated via a practical case.Findings: The performance of various point and interval estimators is compared via the simulation study. Among all proposed estimators, Bayes estimators using MHG algorithm show minimum MSE for all considered censoring schemes. Furthermore, the real data analysis indicates that the splashing diameter decreases with the increase of MPa under different hybrid censored samples.Originality/value: The frequentist and Bayesian methods are developed to estimate the associated parameters of the reliability model under the hybrid censored inverted exponentiated Rayleigh distribution. The application of the proposed stress–strength reliability model will help the reliability engineers and also other scientists to estimate the system reliability.

In this paper, the classical and Bayesian estimation procedures for stress–strength reliability parameter (SSRP) have been considered based on two independent adaptive Type II progressive hybrid censored samples from inverted exponentiated Rayleigh distributions with different shape parameters. The maximum likelihood estimate of SSRP and its asymptotic confidence interval are attained. The Bayes estimate of SSRP is obtained under two loss functions using the Lindley’s approximation and Metropolis–Hastings algorithm. The highest posterior density credible interval is successively constructed. The behavior of suggested estimators is assessed using a simulation study. Finally, the droplet splashing data under two surface wettabilities are considered to illustrate the application of the stress–strength reliability model to the engineering data.