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Goodness-of-fit tests for the Pareto distribution based on its characterization. / Volkova, Ksenia.

в: Statistical Methods and Applications, Том DOI: 10.1007/s10260-015-0330-y, 2015.

Результаты исследований: Научные публикации в периодических изданиях › статья

Harvard

Volkova, K 2015, 'Goodness-of-fit tests for the Pareto distribution based on its characterization', Statistical Methods and Applications, Том. DOI: 10.1007/s10260-015-0330-y.

APA

Volkova, K. (2015). Goodness-of-fit tests for the Pareto distribution based on its characterization. Statistical Methods and Applications, DOI: 10.1007/s10260-015-0330-y.

Vancouver

Volkova K. Goodness-of-fit tests for the Pareto distribution based on its characterization. Statistical Methods and Applications. 2015;DOI: 10.1007/s10260-015-0330-y.

Author

Volkova, Ksenia. / Goodness-of-fit tests for the Pareto distribution based on its characterization. в: Statistical Methods and Applications. 2015 ; Том DOI: 10.1007/s10260-015-0330-y.

BibTeX

@article{e2af973b89fa454c9f3c1f9f1a006e0c,
title = "Goodness-of-fit tests for the Pareto distribution based on its characterization",
abstract = "A new characterization of Pareto distribution is proposed, and new goodness-of-fit tests based on it are constructed. Test statistics are functionals of U-empirical processes. The first of these statistics is of integral type, it is similar to the classical statistics $\omega_n^1.$ The second one is a Kolmogorov type statistic. We show that the kernels of our statistics are non-degenerate. The limiting distribution and large deviations asymptotic of new statistics under null hypothesis are described. Their local Bahadur efficiency for parametric alternatives is calculated. This type of efficiency is mostly appropriate for the solution of our problem since the Kolmogorov type statistic is not asymptotically normal, and the Pitman approach is not applicable to this statistic. For the second statistic we evaluate the critical values by using Monte-Carlo methods. Also conditions of local optimality of new statistics in the sense of Bahadur are discussed and examples of such special alternatives are given. For sma",
keywords = "Pareto distribution, U-statistics, characterization, Bahadur efficiency, goodness-of-fit test",
author = "Ksenia Volkova",
year = "2015",
language = "English",
volume = "DOI: 10.1007/s10260-015-0330-y",
journal = "Statistical Methods and Applications",
issn = "1618-2510",
publisher = "Physica-Verlag",

}

RIS

TY - JOUR

T1 - Goodness-of-fit tests for the Pareto distribution based on its characterization

AU - Volkova, Ksenia

PY - 2015

Y1 - 2015

N2 - A new characterization of Pareto distribution is proposed, and new goodness-of-fit tests based on it are constructed. Test statistics are functionals of U-empirical processes. The first of these statistics is of integral type, it is similar to the classical statistics $\omega_n^1.$ The second one is a Kolmogorov type statistic. We show that the kernels of our statistics are non-degenerate. The limiting distribution and large deviations asymptotic of new statistics under null hypothesis are described. Their local Bahadur efficiency for parametric alternatives is calculated. This type of efficiency is mostly appropriate for the solution of our problem since the Kolmogorov type statistic is not asymptotically normal, and the Pitman approach is not applicable to this statistic. For the second statistic we evaluate the critical values by using Monte-Carlo methods. Also conditions of local optimality of new statistics in the sense of Bahadur are discussed and examples of such special alternatives are given. For sma

AB - A new characterization of Pareto distribution is proposed, and new goodness-of-fit tests based on it are constructed. Test statistics are functionals of U-empirical processes. The first of these statistics is of integral type, it is similar to the classical statistics $\omega_n^1.$ The second one is a Kolmogorov type statistic. We show that the kernels of our statistics are non-degenerate. The limiting distribution and large deviations asymptotic of new statistics under null hypothesis are described. Their local Bahadur efficiency for parametric alternatives is calculated. This type of efficiency is mostly appropriate for the solution of our problem since the Kolmogorov type statistic is not asymptotically normal, and the Pitman approach is not applicable to this statistic. For the second statistic we evaluate the critical values by using Monte-Carlo methods. Also conditions of local optimality of new statistics in the sense of Bahadur are discussed and examples of such special alternatives are given. For sma

KW - Pareto distribution

KW - U-statistics

KW - characterization

KW - Bahadur efficiency

KW - goodness-of-fit test

M3 - Article

VL - DOI: 10.1007/s10260-015-0330-y

JO - Statistical Methods and Applications

JF - Statistical Methods and Applications

SN - 1618-2510

ER -

ID: 5805029