Dominik Szynal (2010)
Banach Center Publications
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Starting from characterizations of continuous distributions in terms of the expected values of two functions of record values we construct a family of goodness-of-fit tests calculated from U-statistics.
Kerwin Morris, Dominik Szynal (2001)
Applicationes Mathematicae
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Using characterization conditions of continuous distributions in terms of moments of order statistics and moments of record values we present new goodness-of-fit techniques.
K. W. Morris, D. Szynal (2002)
Applicationes Mathematicae
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Using characterization conditions of continuous distributions in terms of moments of order statistics given in [12], [23], [6] and [7] we present new goodness-of-fit techniques.
L. Marzec, P. Marzec (1988)
Applicationes Mathematicae
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W. Sadowski (1960)
Applicationes Mathematicae
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Erhard Cramer, Udo Kamps, Mohammad Z. Raqab (2003)
Applicationes Mathematicae
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-3Properties of spacings of generalized order statistics based on IFR and DFR distributions are shown to characterize exponential distributions.
Z. W. Birnbaum, B. P. Lientz (1969)
Applicationes Mathematicae
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Gadasina, L.V. (2005)
Zapiski Nauchnykh Seminarov POMI
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Josef Machek (1959)
Aplikace matematiky
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W. Klonecki (1964)
Colloquium Mathematicae
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Udo Kamps, Ursula Gather (1997)
Applicationes Mathematicae
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Exponential distributions are characterized by distributional properties of generalized order statistics. These characterizations include known results for ordinary order statistics and record values as particular cases.
Miguel Fonseca, Joao Tiago Mexia, Roman Zmyślony (2002)
Discussiones Mathematicae Probability and Statistics
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Generalized F statistics are the quotients of convex combinations of central chi-squares divided by their degrees of freedom. Exact expressions are obtained for the distribution of these statistics when the degrees of freedom either in the numerator or in the denominator are even. An example is given to show how these expressions may be used to check the accuracy of Monte-Carlo methods in tabling these distributions. Moreover, when carrying out adaptative tests, these expressions enable...
L. Weiss (1991)
Metrika
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Haroon Mohamed Barakat, E. M. Nigm, O. M. Khaled (2015)
Kybernetika
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It has been known for a long time that for bootstrapping the distribution of the extremes under the traditional linear normalization of a sample consistently, the bootstrap sample size needs to be of smaller order than the original sample size. In this paper, we show that the same is true if we use the bootstrap for estimating a central, or an intermediate quantile under power normalization. A simulation study illustrates and corroborates theoretical results.