Goodness-of-Fit Tests Based on Spacings for Progressively Type-II Censored Data From a General Location-Scale Distribution

Balakrishnan, N.; Ng, Hon Keung Tony; Kannan, Nandini

doi:10.1109/tr.2004.833317

Cited by 54 publications

(30 citation statements)

References 10 publications

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“…15 We consider 27 different progressive censoring schemes dealt in Balakrishnan et al (2004), which are listed in Tables 1-4. 16 The following lifetime distributions were used as alternatives to the exponential distribution: 3.…”

Section: Application To a Goodness-of-fit Testmentioning

confidence: 99%

Cumulative residual Kullback–Leibler information with the progressively Type-II censored data

Park

Pakyari

2015

Statistics & Probability Letters

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Section: Application To a Goodness-of-fit Testmentioning

confidence: 99%

Cumulative residual Kullback–Leibler information with the progressively Type-II censored data

Park

Pakyari

2015

Statistics & Probability Letters

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“…Several recent papers build on Vasicek's entropy-based test, such as work by Esteban et al [18], Song [19], andŞenoǧlu and Sürücü [20]. Finally, a number of tests of normality based on order statistics have appeared recently, including the work of Glen et al [21] and of Balakrishnan et al [22]. Downloaded by [University of Connecticut] at 20:14 13 October 2014…”

Section: Related Literaturementioning

confidence: 99%

A tool for systematically comparing the power of tests for normality

Breton

DeVore

Brown

2008

Journal of Statistical Computation and Simulation

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In this article, we describe a new approach to compare the power of different tests for normality. This approach provides the researcher with a practical tool for evaluating which test at their disposal is the most appropriate for their sampling problem. Using the Johnson systems of distribution, we estimate the power of a test for normality for any mean, variance, skewness, and kurtosis. Using this characterization and an innovative graphical representation, we validate our method by comparing three well-known tests for normality: the Pearson χ 2 test, the Kolmogorov-Smirnov test, and the D'Agostino-Pearson K 2 test. We obtain such comparison for a broad range of skewness, kurtosis, and sample sizes.We demonstrate that the D'Agostino-Pearson test gives greater power than the others against most of the alternative distributions and at most sample sizes. We also find that the Pearson χ 2 test gives greater power than Kolmogorov-Smirnov against most of the alternative distributions for sample sizes between 18 and 330.

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“…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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Goodness-of-Fit Tests Based on Spacings for Progressively Type-II Censored Data From a General Location-Scale Distribution

Cited by 54 publications

References 10 publications

Cumulative residual Kullback–Leibler information with the progressively Type-II censored data

Cumulative residual Kullback–Leibler information with the progressively Type-II censored data

A tool for systematically comparing the power of tests for normality

Testing Exponentiality Based on the Likelihood Ratio and Power Comparison

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