Results 251 to 260 of about 381,861 (323)
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Order Statistics in Goodness-of-Fit Testing
IEEE Transactions on Reliability, 2001Our new method uses order statistics to judge the fit of a distribution to data. A test-statistic based on quantiles of order-statistics compares favorably with the Kolmogorov-Smirnov (K-S) and Anderson-Darling (A-D) test statistics. The performance of this new goodness-of-fit test statistic is examined with simulation experiments.
Andrew G. Glen +2 more
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Random Numbers Generation and Goodness-of-Fit Testing for Literal Neutrosophic Numbers
Galoitica: Journal of Mathematical Structures and Applications, 2023This paper presents an algorithm for generating random numbers that follow literal neutrosophic distributions using algebraic isomorphisms. The algorithm was applied to three distributions: literal neutrosophic uniform distribution, literal neutrosophic ...
M. B. Zeina +2 more
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Smooth Tests of Goodness of Fit
Technometrics, 1991AbstractSmooth tests of goodness of fit assess the fit of data to a given probability density function within a class of alternatives that differs ‘smoothly’ from the null model. These alternatives are characterized by their order: the greater the order the richer the class of alternatives. The order may be a specified constant, but data‐driven methods
Rayner, J. C. W., Thas, O., Best, D. J.
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Integral transform methods in goodness-of-fit testing, II: the Wishart distributions
Annals of the Institute of Statistical Mathematics, 2019We initiate the study of goodness-of-fit testing for data consisting of positive definite matrices. Motivated by the appearance of positive definite matrices in numerous applications, including factor analysis, diffusion tensor imaging, volatility models
Elena Hadjicosta, D. Richards
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Copula goodness-of-fit testing: an overview and power comparison
European Journal of Finance, 2009 Daniel Berg
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Length tests for goodnesss-of-fit
Biometrika, 1991Consider an i.i.d. sample X 1,..., X n with distribution function F, which throughout is assumed to be twice continuously differentiable with support [0,1] and strictly positive derivative on [0,1]. Denote by $$0={X_{0:n}}\leqslant {X_{1:n}}\leqslant\cdots\leqslant{X_{n:n}}\leqslant{X_{n+1:n}}=1$$ (1) the order statistics, and the spacings by
Reschenhofer, Erhard, Bomze, Immanuel
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Goodness-of-Fit Testing for the Degradation Models in Reliability Analysis
IEEE International Conference on Actual Problems of Electronics Instrument Engineering, 2018In this paper, the gamma and Wiener degradation models with covariates are considered. It is assumed that the independent increments of the degradation index have gamma distribution for the gamma degradation model and normal distribution in case of the ...
E. Chimitova, E. S. Chetvertakova
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Journal of Multivariate Analysis
We present a novel data-oriented statistical framework that assesses the presumed Gaussian dependence structure in a pairwise setting. This refers to both multivariate normality and normal copula goodness-of-fit testing.
Jakub Wo'zny +4 more
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We present a novel data-oriented statistical framework that assesses the presumed Gaussian dependence structure in a pairwise setting. This refers to both multivariate normality and normal copula goodness-of-fit testing.
Jakub Wo'zny +4 more
semanticscholar +1 more source
Goodness-of-Fit Tests on a Circle. II
Biometrika, 1961Abstract : A statistical analysis is made by use of the null hypothesis test for random samples which have been drawn from a population with the continuous distribution function F(x). It is useful for distributions on a circle since its value does not depend on the arbitrary point chosen to begin cumulating the probability density and the sample points.
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Statistica Neerlandica, 1966
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Vandewiele, Georges, De Witte, Paul
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Vandewiele, Georges, De Witte, Paul
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