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231226s2020 xx |||||o 00| ||eng c |
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|a 10.1080/02664763.2019.1675608
|2 doi
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|a eng
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|a Witkovský, Viktor
|e verfasserin
|4 aut
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|a Computing the exact distribution of the Bartlett's test statistic by numerical inversion of its characteristic function
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|c 2020
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 16.06.2022
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|a published: Electronic-eCollection
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|a Citation Status PubMed-not-MEDLINE
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|a © 2019 Informa UK Limited, trading as Taylor & Francis Group.
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|a Application of the exact statistical inference frequently leads to non-standard probability distributions of the considered estimators or test statistics. The exact distributions of many estimators and test statistics can be specified by their characteristic functions, as is the case for the null distribution of the Bartlett's test statistic. However, analytical inversion of the characteristic function, if possible, frequently leads to complicated expressions for computing the distribution function and the corresponding quantiles. An efficient alternative is the well-known method based on numerical inversion of the characteristic functions, which is, however, ignored in popular statistical software packages. In this paper, we present the explicit characteristic function of the corrected Bartlett's test statistic together with the computationally fast and efficient implementation of the approach based on numerical inversion of this characteristic function, suggested for evaluating the exact null distribution used for testing homogeneity of variances in several normal populations, with possibly unequal sample sizes
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|a Journal Article
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|a 62E15
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|a 62H10
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|a Bartlett's test statistic
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| 650 |
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4 |
|a characteristic function
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4 |
|a exact distribution
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4 |
|a numerical inversion
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|i Enthalten in
|t Journal of applied statistics
|d 1991
|g 47(2020), 13-15 vom: 09., Seite 2749-2764
|w (DE-627)NLM098188178
|x 0266-4763
|7 nnns
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| 773 |
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|g volume:47
|g year:2020
|g number:13-15
|g day:09
|g pages:2749-2764
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|u http://dx.doi.org/10.1080/02664763.2019.1675608
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