On flexible progressive censoring

Bairamov, Ismihan; Parsi, Safar

doi:10.1016/j.cam.2010.02.041

Cited by 10 publications

(14 citation statements)

References 21 publications

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“…An extension of Type-II progressive censoring has been introduced by Bairamov and Parsi (2011). The extension of Bairamov's scheme has been given by Kinaci (2013).…”

Section: Kinaci's Methodsmentioning

confidence: 99%

Type-II stepwise progressive censoring

Bayat

Torabi

2016

CSAM

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Type-II progressive censoring is one of the censoring methods frequently used in clinical studies, reliability trials, quality control of products and industrial experiments. Sometimes in Type-II progressive censoring experiments, the failure rate is low so the waiting time to observe the m th failure will be very long; however, the experimenter may have to terminate the experiment before a predetermined time. In this article, if two generalized types of Type-II progressive censoring are reminded, we then make some changes in the removal method of Type-II progressive censoring such that without reducing the deduction quality, the termination time of the experiment decreases. This can be done with decreasing withdraws throughout the steps of the experiment with a special reasonable method. A simulation study is done and the results are tabulated at the end of this article for a comparison between introduced method and Type-II progressive censoring.

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“…An extension of Type-II progressive censoring has been introduced by Bairamov and Parsi (2011). The extension of Bairamov's scheme has been given by Kinaci (2013).…”

Section: Kinaci's Methodsmentioning

confidence: 99%

Type-II stepwise progressive censoring

Bayat

Torabi

2016

CSAM

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“…Example 2 Bairamov and Parsi [2] generalized the experiment of [16] by considering two (fixed) progressive censoring schemes…”

Section: Adaptive Progressive Type-ii Censoringmentioning

confidence: 99%

“…, 0), and T 1 = · · · = T m−1 ≡ T , this reduces to the adaptive scheme of [16]. Kinaci [12] extended the model of [2] by considering k + 1 (fixed) progressive censoring schemes, r…”

Section: Adaptive Progressive Type-ii Censoringmentioning

confidence: 99%

“…In the Ng-Kundu-Chan model [16], a threshold parameter T is used to switch from the initially planned scheme to a modified scheme (for details, see Example 1). This idea has been taken up in [2,12], where some extensions of this basic idea are presented. The general theory of adaptive progressive Type-II censoring is developed in [9].…”

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confidence: 98%

“…2 Generation process of progressively Type-I censored order statistics standard models of progressive censoring are progressive Type-I and progressive Type-II censoring as first presented in [8]. In these models, the censoring plan is fixed in advance but the censoring times are…”

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confidence: 99%

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Adaptive Progressive Censoring

Cramer

Iliopoulos

2015

Ordered Data Analysis, Modeling and Health Research Methods

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The notion of adaptive progressive Type-II censoring has been introduced in Cramer and Iliopoulos (2010) to analyse data from a progressively Type-II censored life test with observation dependent removals of units. Such a scheme gives more flexibility to the experimenter since it allows him/her to choose the number of units to be removed at each failure time during the life test. In this paper, the idea is generalised to a more general setting of progressive censoring. Our generalised model allows for arbitrary inspection times and possible removals of units during the experiment. The inspection times and removals depend on what has been observed so far. In particular, this approach includes adaptive progressive Type-I and Type-II censoring with random or fixed inspection timepoints.

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Generalized Ng–Kundu–Chan model of adaptive progressive Type‐II censoring and related inference

Schmiedt,

Cramer

2023

Naval Research Logistics

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The model of adaptive progressive Type‐II censoring introduced by Ng et al. (2009) (referred to as Ng–Kundu–Chan model) is extended to allow switching from a given initial censoring plan to any arbitrary given plan of the same length. In this generalized model, the joint distribution of the failure times and the corresponding likelihood function is derived. It is illustrated that the computation of maximum likelihood and Bayesian estimates are along the same lines as for standard progressive Type‐II censoring. However, the distributional properties of the estimators will usually be different since the censoring plan actually applied in the (generalized) Ng–Kundu–Chan model is random. As already mentioned in Cramer and Iliopoulos (2010), we directly show that the normalized spacings are independent and identically exponentially distributed. However, it turns out that the spacings themselves are generally dependent with mixtures of exponential distributions as marginals. These results are used to study linear estimators. Finally, we propose an algorithm for generating random numbers in the generalized Ng–Kundu–Chan model and present some simulation results. The results obtained also provide new findings in the original Ng–Kundu–Chan model; the corresponding implications are highlighted.

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On flexible progressive censoring

Cited by 10 publications

References 21 publications

Type-II stepwise progressive censoring

Type-II stepwise progressive censoring

Adaptive Progressive Censoring

Generalized Ng–Kundu–Chan model of adaptive progressive Type‐II censoring and related inference

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