Statistical power of goodness-of-fit tests based on the empirical distribution function for type-I right-censored data

Bispo, Regina; Marques, Tiago A.; Pestana, Dínis

doi:10.1080/00949655.2011.624519

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…There are several goodness-of-fit tests available in the literature based on a complete sample and an excellent overview on this topic can be found in D' Agostino and Stephens (1986). Nevertheless, there has been relatively little work done on the problem of goodness-of-fit for Type I censored data and attention was usually paid only to right censoring (Bispo, Marques, and Pestana 2011;Pakyari and Balakrishnan 2013;Pakyari and Nia 2017). In this paper, we focus on Type I left-censored data and three tests (Kolmogorov-Smirnov, Cramér-von Mises, Anderson-Darling) based on the empirical distribution function (EDF).…”

Section: Introductionmentioning

confidence: 99%

“…It can bring readers valuable information about the type II error that can be expected when having a dataset with a specific size and a number of censored values. A similar study was carried out by Bispo et al (2011) for rightcensored data, although, only for the completely specified alternative distributions. This is usually not the case when modelling real data.…”

Section: Introductionmentioning

confidence: 99%

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Statistical Power of Goodness-of-Fit Tests for Type~I Left-Censored Data

Fusek

2023

AJS

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Type I doubly left-censored data often arise in environmental studies. In this paper, the power of the most frequently used goodness-of-fit tests (Kolmogorov-Smirnov, Cramér-von Mises, Anderson-Darling) is studied considering various sample sizes and degrees of censoring. Attention is paid to testing of the composite hypothesis that the data has a specific distribution with unknown parameters, which are estimated using the maximum likelihood method. Performance of the tests is assessed by means of Monte Carlo simulations for several distributions, specifically the Weibull, lognormal and gamma distributions, which are among the most frequently used distributions for modelling of environmental data. Finally, the tests are used for identification of the distribution of musk concentrations if fish tissue.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistical Power of Goodness-of-Fit Tests for Type~I Left-Censored Data

Fusek

2023

AJS

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“…First and second order departures from normality depend on the skewness and kurtosis of the distribution, we have used 72 alternatives with wider ranges of these parameters. This alternative space includes mixture of uniform distributions, mixture of t-distributions and the distributions used in the literature [ 14 , 16 – 18 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Min-max approach for comparison of univariate normality tests

Islam

2021

PLoS ONE

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Comparison of normality tests based on absolute or average powers are bound to give ambiguous results, since these statistics critically depend upon the alternative distribution which cannot be specified. A test which is optimal against a certain type of alternatives may perform poorly against other alternative distributions. Thus, an invariant benchmark is proposed in the recent normality literature by computing Neyman-Pearson tests against each alternative distribution. However, the computational cost of this benchmark is significantly high, therefore, this study proposes an alternative approach for computing the benchmark. The proposed min-max approach reduces the calculation cost in terms of computing and estimating the Neyman-Pearson tests against each alternative distribution. An extensive simulation study is conducted to evaluate the selected normality tests using the proposed methodology. The proposed min-max method produces similar results in comparison with the benchmark based on Neyman-Pearson tests but at a low computational cost.

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“…Since I plan to use numerical methods, the alternative (non-normal) space must be narrowed down to something sufficiently small to permit exploration by numerical methods. At the same time, the space should be large enough to provide a good approximation to the full space of alternatives -failing that, it should be large enough to approximate the distributions conventionally used in simulations studies to assess the performance of normality tests [12]; Pearson et al [13]; Thadewald et al [14], Zhang, et al [15], Yazici, et al [16], Romao et al [17], Yap, et al [18] and Bispo, et al [19], Islam [20]. The distributions used as alternative space cover a wide range of real world applications in the field of Social Sciences, Genomics, Neuro Sciences and Baysian Econometrics modelling.…”

Section: Introductionmentioning

confidence: 99%

Preliminary Tests of Homogeneity - Type I Error Rates under Non-Normality

Islam¹

2018

BBOAJ

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Many statistical procedures utilize preliminary tests to enhance the accuracy of the final inferences. Preliminary tests like Goldfeld-Quandt (GQ) and Levene-type tests are used to assess the assumption of equality of population variances with normality as the underlying distributional assumption. Such tests must be used with care as the final inferences are conditional on the performance of these tests at first stage. This study explores the size distortions of GQ and Levene-type tests under non-normality. The results do not warrant the use of GQ & Levene test under non-normality as the size distortions are as high as 88 & 48% for the respective statistics. However, the modified form of Levene test (BF-test) retains its size properties except for the multi-model alternatives with relatively big outliers.

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Statistical power of goodness-of-fit tests based on the empirical distribution function for type-I right-censored data

Cited by 7 publications

References 7 publications

Statistical Power of Goodness-of-Fit Tests for Type~I Left-Censored Data

Statistical Power of Goodness-of-Fit Tests for Type~I Left-Censored Data

Min-max approach for comparison of univariate normality tests

Preliminary Tests of Homogeneity - Type I Error Rates under Non-Normality

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