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

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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 ℛ to any arbitrary given plan 𝒮 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.

Language: Englisch

Bibliographic citation: Journal: Naval Research Logistics (NRL) ; ISSN: 1520-6750 ; Volume: 71 ; Year: 2023 ; Issue: 3 ; Pages: 389-415 ; Hoboken, USA: John Wiley & Sons, Inc.

Classification: Management

Subject: adaptive progressive censoring
exponential distribution
likelihood inference
linear inference
Ng–Kundu–Chan model
progressive Type‐II censoring
spacings

Event: Geistige Schöpfung

(who): Schmiedt, Anja Bettina
Cramer, Erhard

Event: Veröffentlichung

(who): John Wiley & Sons, Inc.

(where): Hoboken, USA

(when): 2023

DOI: doi:10.1002/nav.22152

Last update: 10.03.2025, 11:41 AM CET

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Object type

Artikel

Associated

Schmiedt, Anja Bettina
Cramer, Erhard
John Wiley & Sons, Inc.

Time of origin

2023

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