A General Purpose Approximate Goodness-of-Fit Test for Progressively Type-II Censored Data

Pakyari, Reza; Balakrishnan, N.

doi:10.1109/tr.2012.2182811

Cited by 85 publications

(42 citation statements)

References 21 publications

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“…The modified Kolmogorov-Smirnov statistic for progressive type-II censored data was suggested by Pakyari and Balakrishnan (2012). They proposed a general purpose goodness-of-fit test by first estimating the unknown parameters of the hypothesized distribution, then transforming the data to normality, and then testing the goodnessof-fit of the transformed data to normality.…”

Section: Examplementioning

confidence: 99%

“…They proposed a general purpose goodness-of-fit test by first estimating the unknown parameters of the hypothesized distribution, then transforming the data to normality, and then testing the goodnessof-fit of the transformed data to normality. For further information about the test procedure and how to transform the data to normality (Pakyari and Balakrishnan, 2012). Let T 1:m:n < T 2:m:n < · · · < T m:m:n be a progressively type-II right censored sample with progressive censoring scheme (R 1 , R 2 , .…”

Section: Examplementioning

confidence: 99%

“…. , m. Based on the technique of Pakyari and Balakrishnan (2012) for transforming the data to normality and then testing the goodness-of-fit of the transformed data to normality, the values of test statistic (D m:n ) and the corresponding p-values for each stress level S i , i = 1, 2, are calculated and presented in Table 2. It is clear that the EE distributions provide a good fit to the given data due to all p-values be greater than 0.05.…”

Section: Examplementioning

confidence: 99%

See 2 more Smart Citations

Parametric inference on step-stress accelerated life testing for the extension of exponential distribution under progressive type-II censoring

El-Din

Abu-Youssef

Ali

et al. 2016

CSAM

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In this paper, a simple step-stress accelerated life test (ALT) under progressive type-II censoring is considered. Progressive type-II censoring and accelerated life testing are provided to decrease the lifetime of testing and lower test expenses. The cumulative exposure model is assumed when the lifetime of test units follows an extension of the exponential distribution. Maximum likelihood estimates (MLEs) and Bayes estimates (BEs) of the model parameters are also obtained. In addition, a real dataset is analyzed to illustrate the proposed procedures. Approximate, bootstrap and credible confidence intervals (CIs) of the estimators are then derived. Finally, the accuracy of the MLEs and BEs for the model parameters is investigated through simulation studies.

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Section: Examplementioning

confidence: 99%

Section: Examplementioning

confidence: 99%

Section: Examplementioning

confidence: 99%

See 1 more Smart Citation

Parametric inference on step-stress accelerated life testing for the extension of exponential distribution under progressive type-II censoring

El-Din

Abu-Youssef

Ali

et al. 2016

CSAM

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show abstract

“…For example, see D'Agostino and Stephens [8] and Huber-Carol et al [15]. Moreover, Ebrahimi [11,12], Balakrishnan et al [4,5], Park [22], Lim and Park [18], Lin et al [19], Habibi Rad et al [14], and Pakyari and Balakrishnan [20,21] developed some tests based on censored samples.…”

Section: Introductionmentioning

confidence: 99%

Testing Exponentiality Based on the Likelihood Ratio and Power Comparison

Noughabi

2015

Ann. Data. Sci.

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The exponential distribution is one of the fundamental lifetime models and is widely used for describing a failure mechanism of a system. Different applications of this distribution in survival analysis and reliability theory can be found in statistical literature. In this article, some powerful tests for exponentiality based on the likelihood ratio are proposed. The critical points of the test statistics are obtained by Monte Carlo simulations. The power values of the proposed tests are computed against a wide variety of alternative hypotheses and then these values are compared with the power values of the recent published exponentiality tests. It is shown that these tests have a reasonable power for various kinds of departures from exponentiality. For illustrative purpose, real examples are finally presented.

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“…Moreover, some tests for censored data are proposed by authors; see, for example, [10,11,21,25,29,30].…”

Section: Introductionmentioning

confidence: 99%

A General Statistic for Testing the Validity of a Model’s Forecasts

Noughabi

2015

Ann. Data. Sci.

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The paper introduces a new approach for testing the validity of a model's forecasts and it is established that the proposed test is exact for location-scale family. The results are used to introduce goodness-of-fit tests for the normal, exponential, uniform and Laplace distributions. Through Monte Carlo simulation, the power values of the proposed test under various alternatives are compared with the other tests. The proposed test has higher power than the competing tests against some alternatives. The use of the proposed test is shown in real examples.

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A General Purpose Approximate Goodness-of-Fit Test for Progressively Type-II Censored Data

Cited by 85 publications

References 21 publications

Parametric inference on step-stress accelerated life testing for the extension of exponential distribution under progressive type-II censoring

Parametric inference on step-stress accelerated life testing for the extension of exponential distribution under progressive type-II censoring

Testing Exponentiality Based on the Likelihood Ratio and Power Comparison

A General Statistic for Testing the Validity of a Model’s Forecasts

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