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The present article develops the generalized multiple dependent state sampling plan for testing the quality of the ocean data. The plan parameters for the inspection of the ocean data are selected through the optimization process. The operational procedure for the inspection of ocean data is given under the proposed sampling scheme. The efficiency of the proposed plan is compared with the existing plan used for the inspection of the quality of ocean data in terms of sample size. From the comparative study, it is concluded that the proposed plan is efficient than the existing plan in an inspection of the ocean data. [ABSTRACT FROM AUTHOR]

We present here a simple probabilistic approach for determining an optimal progressive censoring scheme by defining a probability structure on the set of feasible solutions. Given an initial solution, the new updated solution is computed within the probabilistic structure. This approach will be especially useful when the cardinality of the set of feasible solutions is large. The validation of the proposed approach is demonstrated by comparing the optimal scheme so determined with the one obtained by exhaustive numerical search. [ABSTRACT FROM AUTHOR]

We study a competing risks model using Gompertz distribution under progressive Type-II censoring when probability distributions of failure causes are identically distributed with common scale and different shape parameters. Maximum likelihood estimates (MLEs) of these parameters are obtained and their uniqueness and existence behavior are also discussed. The asymptotic intervals are derived from the observed Fisher information matrix. We compare the performance of all the estimators numerically using simulations. Analysis of a real data set is presented as well. We further determine optimal censoring scheme using expected Fisher information matrix. The design parameters are selected based on suitable measures like cost-based and variance-based criteria functions. Finally, we discuss single and multi-objective optimization approaches to find optimal censoring schemes. [ABSTRACT FROM AUTHOR]

We consider determination of optimal warranty length for the combined free replacement and pro-rata warranty (FRW-PRW) policy based on the Type-II unified hybrid censored data by Bayesian approach. A non-linear pro-rata rebate cost function is proposed based on which warranty cost is computed. It is assumed that the lifetime follows log normal distribution. The optimal warranty length is obtained by maximizing an expected utility function consisting of three cost functions such as economic benefit function, warranty cost function and dissatisfaction cost function. The expectation is taken with respect to the posterior predictive model for the time-to-failure data. It is observed that the non-linear pro-rata rebate cost function gives a larger warranty length with maximum profit as compared to linear pro-rata rebate cost function. A real-data set is analyzed in order to illustrate the proposed methodology of finding optimal warranty length. [ABSTRACT FROM AUTHOR]

In many research studies, multiple objectives need to be considered simultaneously to ensure an effective and efficient investigation. A compound optimal design provides a viable solution to this problem, allowing for the maximization of overall benefits through the integration of several factors. The paper addresses the application of compound optimal designs in the context of progressive first‐failure censoring, with a particular focus on the Generalized Exponential distribution with two parameters. The paper provides an illustrative example of compound designs by considering the cost function along with trace, variance, and determinant of inverse Fisher information. The best design is determined using a graphical solution technique that is both comprehensible and precise. Using a simple example, we demonstrate the advantage of compound optimal designs over constraint optimal designs. Furthermore, the paper examines real‐world data collection to demonstrate the practical utility of compound optimal designs. [ABSTRACT FROM AUTHOR]

This article describes the inferential procedures and Bayesian optimal life‐testing issues under Type‐II unified hybrid censoring scheme. First, the explicit expressions of expected number of failures, expected duration of testing, and Fisher information matrix for the unknown parameters of the underlying lifetime model are derived. Then, using these quantities, the Bayesian optimal life‐testing plans are computed in a subsequent section. A cost constraint D‐optimal optimization problem has been formulated, and the corresponding solution algorithm is provided to obtain optimal plans. Computational procedures are illustrated through numerical examples. [ABSTRACT FROM AUTHOR]

This article considers inference based on Type-II progressive hybrid censored data for a generalized exponential distribution. The maximum likelihood (ML) estimates, Bayes estimates and corresponding interval estimates of unknown model parameters are derived. Prediction estimates and prediction intervals of censored observations are obtained under one- and two-sample Bayesian framework. A Monte Carlo simulation study is undertaken to compare the proposed methods of estimation. A real data set is analyzed for illustration purpose. Finally, optimal life testing plans are obtained under cost constraints using two different optimality criteria. A computational algorithm is proposed to compute the optimal plans. [ABSTRACT FROM AUTHOR]