Average type‐II censoring times for two‐parameter Pareto and Rayleigh distributions

Chiou, Kuo Ching; Tong, Lee‐Ing

doi:10.1108/02656710110393538

Cited by 7 publications

(1 citation statement)

References 8 publications

(10 reference statements)

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“…A common feature of the life time datasets is the presence of censoring. Among various censoring schemes, an extensive amount of literature exists on Type-I and Type-II censoring schemes (see, for example, Chiou and Tong, 2001; Tsai and Lin, 2009). Type-I and Type-II censoring schemes are easy to implement in industrial applications.…”

Section: Introductionmentioning

confidence: 99%

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

Roy

Pradhan

Purakayastha³

2021

IJQRM

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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.

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Section: Introductionmentioning

confidence: 99%

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

Roy

Pradhan

Purakayastha³

2021

IJQRM

View full text Add to dashboard Cite

show abstract

Predicting type II censored data from factorial experiments using modified maximum likelihood predictor

Yang

Tong

2006

Int J Adv Manuf Technol

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Analyzing type II censored data obtained from repetitious experiments

Tong¹,

Yang²

2006

Journal of Applied Statistics

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Experimental design and Taguchi's parameter design are widely employed by industry to optimize the process/product. However, censored data are often observed in product lifetime testing during the experiments. After implementing a repetitious experiment with type II censored data, the censored data are usually estimated by establishing a complex statistical model. However, using the incomplete data to fit a model may not accurately estimates the censored data. Moreover, the model fitting process is complicated for a practitioner who has only limited statistical training. This study proposes a less complex approach to analyze censored data, using the least square estimation method and Torres's analysis of unreplicated factorials with possible abnormalities. This study also presents an effective method to analyze the censored data from Taguchi's parameter design using least square estimation method. Finally, examples are given to illustrate the effectiveness of the proposed methods.Type II censored data, least square estimation, Torres's method, experimental design, Taguchi's parameter design,

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Average type‐II censoring times for two‐parameter Pareto and Rayleigh distributions

Cited by 7 publications

References 8 publications

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

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

Predicting type II censored data from factorial experiments using modified maximum likelihood predictor

Analyzing type II censored data obtained from repetitious experiments

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