Browsing Research Output by Subject "Goodness-of-fit"
Now showing items 1-6 of 6
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A class of goodness-of-fit tests based on a new characterization of the exponential distribution
(Taylor & Francis, 2008)A new characterisation of the exponential distribution in the class of new better than used in expectation (NBUE) life distributions is presented. Utilising this characterisation, a new class of goodness-of-fit tests for ... -
A class of goodness-of-fit tests for circular distributions based on trigonometric moments
(IDESCAT, 2019)We propose a class of goodness–of–fit test procedures for arbitrary parametric families of circular distributions with unknown parameters. The tests make use of the specific form of the characteristic function of the ... -
A Monte Carlo evaluation of the performance of two new tests for symmetry
(Springer, 2017)We propose two new tests for symmetry based on well-known characterisations of symmetric distributions. The performance of the new tests is evaluated and compared to that of other existing tests by means of a Monte Carlo ... -
On a new goodness-of-fit test for the Rayleigh distribution based on a conditional expectation characterization
(Taylor & Francis, 2020)We propose and study new goodness-of-fit tests for the Rayleigh distribution based on a characterization involving a conditional expectation. The asymptotic properties of the tests are explored and the performance of the ... -
A review of testing procedures based on the empirical characteristic function
(SASA, 2016)The empirical characteristic function (ECF) has been in use in statistical inference for nearly fifty years now. We provide an overview of testing procedures based on the ECF within certain statistical ... -
Testing for spherical symmetry via the empirical characteristic function
(Taylor & Francis, 2014)Kolmogorov–Smirnov-type and Cramér–von Mises-type goodness-of-fit tests are proposed for the null hypothesis that the distribution of a random vector X is spherically symmetric. The test statistics utilize the fact that X ...