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.
L. Marzec, P. Marzec (1988)
Applicationes Mathematicae
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C.B. Bell, Y. Rama Krishna Sarma (1980)
Metrika
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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.
Z. W. Birnbaum, B. P. Lientz (1969)
Applicationes Mathematicae
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G. S. Lingappaiah (1983)
Applicationes Mathematicae
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M.M. Mohie El-Din, S.E. Abu-Youssef, K.S. Sultan (1996)
Metrika
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L. Weiss (1991)
Metrika
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J. Bartoszewicz, R. Zieliński (1985)
Applicationes Mathematicae
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Dominik Szynal, Waldemar Wołyński (2014)
Discussiones Mathematicae Probability and Statistics
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We discuss two families of tests for normality based on characterizations of continuous distributions via order statistics and record values. Simulations of their powers show that they are competitive to widely recommended tests in the literature.
U. Kamps (1991)
Metrika
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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.
Z. Grudzień, D. Szynal (1999)
Applicationes Mathematicae
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Power distributions can be characterized by equalities involving three moments of order statistics. Similar equalities involving three moments of k-record values can also be used for such a characterization. The case of samples with random sizes is also considered.
J. Bartoszewicz (1987)
Applicationes Mathematicae
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Ali İ. Genç (2015)
Discussiones Mathematicae Probability and Statistics
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We derive an explicit expression for the single moments of order statistics from the generalized t (GT) distribution. We also derive an expression for the product moment of any two order statistics from the same distribution. Then the location-scale estimating problem of a real data set is solved alternatively by the best linear unbiased estimates which are based on the moments of order statistics.