On inference and design under progressive type-I interval censoring scheme for inverse Gaussian lifetime model

Abstract

PurposeThis article considers Inverse Gaussian distribution as the basic lifetime model for the test units. The unknown model parameters are estimated using the method of moments, the method of maximum likelihood and Bayesian methods. As part of maximum likelihood analysis, this article employs an expectation-maximization algorithm to simplify numerical computation. Subsequently, Bayesian estimates are obtained using the Metropolis–Hastings algorithm. This article then presents the design of optimal censoring schemes using a design criterion that deals with the precision of a particular system lifetime quantile. The optimal censoring schemes are obtained after taking into account budget constraints.Design/methodology/approachThis article first presents classical and Bayesian statistical inference for Progressive Type-I Interval censored data. Subsequently, this article considers the design of optimal Progressive Type-I Interval censoring schemes after incorporating budget constraints.FindingsA real dataset is analyzed to demonstrate the methods developed in this article. The adequacy of the lifetime model is ensured using a simulation-based goodness-of-fit test. Furthermore, the performance of various estimators is studied using a detailed simulation experiment. It is observed that the maximum likelihood estimator relatively outperforms the method of moment estimator. Furthermore, the posterior median fares better among Bayesian estimators even in the absence of any subjective information. Furthermore, it is observed that the budget constraints have real implications on the optimal design of censoring schemes.Originality/valueThe proposed methodology may be used for analyzing any Progressive Type-I Interval Censored data for any lifetime model. The methodology adopted to obtain the optimal censoring schemes may be particularly useful for reliability engineers in real-life applications.

Inference for log‐location‐scale family of distributions under competing risks with progressive type‐I interval censored data

“…Later, Lin and Lio (2012) provide Bayesian inference for PIC-I datasets for Weibull and generalized exponential lifetime models. Subsequently, statistical inference for PIC-I data sets is also considered for the log-normal distribution (Roy, Gijo, & Pradhan, 2017), Burr distribution (Belaghi, Noori Asl, & Singh, 2017), inverse Weibull distribution (Singh & Tripathi, 2018), truncated Normal distribution (Lodhi & Tripathi, 2020), and inverse Gaussian distribution (Roy, Pradhan, & Purakayastha, 2022).…”