Design of optimal step–stress accelerated life tests under progressive type I censoring with random removals

Shen, Kuan-Fu; Shen, Yu-Jan; Leu, Lii-Yuh

doi:10.1007/s11135-010-9315-y

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…Here, the more general progressively type‐I censoring scheme is considered, whereas the Bayesian approach is adopted. For the role of progressive censoring in SSALT experiments and associated designs, we refer to the works of Gouno et al, Fan et al, and Shen et al Furthermore, note that, in the work of Bobotas and Kateri, the shape parameters of the underlying lifetime distributions at each stress level were assumed to be known while they can be estimated here as well. Moreover, the shape parameter may not be necessarily constant over all stress levels, as is usually assumed.…”

Section: Introductionmentioning

confidence: 99%

Bayesian analysis for step‐stress accelerated life testing under progressive interval censoring

Kohl

Kateri

2019

Appl Stoch Models Bus & Ind

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A step‐stress accelerated life testing model is considered for progressive type‐I censored experiments when the tested items are not monitored continuously but inspected at prespecified time points, producing thus grouped data. The underlying lifetime distributions belong to a general scale family of distributions. The points of stress‐level change are simultaneously inspection points as well while there is the option of assigning additional inspection points in between the stress‐level change points. In a Bayesian framework, the posterior distributions of the parameters of the model are derived for characteristic choices of prior distributions, as conjugate‐like and normal priors; vague or noninformative. The developed approach is illustrated on a simulated example and on a real data set, both known from the literature. The results are compared to previous analyses; frequentist or Bayes.

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Section: Introductionmentioning

confidence: 99%

Bayesian analysis for step‐stress accelerated life testing under progressive interval censoring

Kohl

Kateri

2019

Appl Stoch Models Bus & Ind

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“…, Shen et al . , and Wu and Huang considered determination of optimum life test plans for different generalization of the conventional PIC‐I under accelerated life tests and competing risks data.…”

Section: Introductionmentioning

confidence: 99%

Computing optimum design parameters of a progressive type I interval censored life test from a cost model

Budhiraja

Pradhan

2017

Appl Stoch Models Bus & Ind

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This work considers optimum design of a life testing experiment with progressive type I interval censoring. A cost minimizationbased optimality criterion is proposed. The proposed cost function incorporates the cost of conducting the experiment, opportunity cost, and post-sale cost. It is shown that the proposed cost function is scale invariant for any lifetime distribution whose support does not depend on the parameters of the distribution. Weibull distribution is considered for illustration. Optimum solution is obtained by a suitable numerical method. A sensitivity analysis is undertaken to study the effect of small perturbations in lifetime model parameter values or cost coefficients.

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“…Recently, progressive type I interval censoring has received the attention of many authors. Some important literature can be found, for example see (Aggarwala, 2001;Ding et al, 2010;Gouno et al, 2004;Huang and Wu, 2008;Shen et al, 2011;Wu et al, 2006Wu et al, , 2008Xiang and Tse, 2005;Yang and Tse, 2005).…”

Section: Introductionmentioning

confidence: 99%

Optimum Partially Accelerated Life Test Plans with Progressively Type I Interval-Censored Data

İsmail

2015

Sequential Analysis

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Because of continual improvement in manufacturing design, one often deals with high quality products that are highly reliable with a substantially long life span. This article discusses k-level step-stress partially accelerated tests under type I progressive interval censoring with equal inspection intervals of length . It is assumed that the lifetime of a testing unit follows a Weibull distribution. The problem of choosing the optimal is considered according to a certain optimality criterion. Two selection criteria that enable us to obtain the optimum test plans are investigated. Monte Carlo simulations are presented to illustrate the proposed methods.

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Design of optimal step–stress accelerated life tests under progressive type I censoring with random removals

Abstract: Progressive type I censoring with random removal (PCRR), Step–stress, Accelerated life testing, Asymptotic variance, Variance (V)-optimality, Determinant (D)-optimality,

Cited by 8 publications

References 13 publications

Bayesian analysis for step‐stress accelerated life testing under progressive interval censoring

Bayesian analysis for step‐stress accelerated life testing under progressive interval censoring

Computing optimum design parameters of a progressive type I interval censored life test from a cost model

Optimum Partially Accelerated Life Test Plans with Progressively Type I Interval-Censored Data

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