Results 161 to 170 of about 400,578 (220)
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2018
Based on the substitution principle, we derive one-sample goodness-of-fit tests of Kolmogorov-Smirnov and Cramer-von Mises type, respectively. In the case of a completely specified null hypothesis, these tests are distribution-free, if the cumulative distribution function under the null is a continuous function. In the case of composite null hypotheses,
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Based on the substitution principle, we derive one-sample goodness-of-fit tests of Kolmogorov-Smirnov and Cramer-von Mises type, respectively. In the case of a completely specified null hypothesis, these tests are distribution-free, if the cumulative distribution function under the null is a continuous function. In the case of composite null hypotheses,
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Journal of Statistical Computation and Simulation, 2019
The aim of this article is to review existing goodness-of-fit tests for the exponential distribution under progressive Type-II censoring and to provide some new ideas and adjustments.
M. Döring, E. Cramer
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The aim of this article is to review existing goodness-of-fit tests for the exponential distribution under progressive Type-II censoring and to provide some new ideas and adjustments.
M. Döring, E. Cramer
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1998
Goodness-of-fit tests are used to validate the use of a particular distribution to describe data arising from sampling or experimentation. Numerous goodness-of-fit tests have been developed. The power divergence family of test statistics includes Pearson’s chi-squared test, the likelihood ratio test, and the Freeman-Tukey chi-squared test.
Linda J. Young, Jerry H. Young
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Goodness-of-fit tests are used to validate the use of a particular distribution to describe data arising from sampling or experimentation. Numerous goodness-of-fit tests have been developed. The power divergence family of test statistics includes Pearson’s chi-squared test, the likelihood ratio test, and the Freeman-Tukey chi-squared test.
Linda J. Young, Jerry H. Young
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2001
Goodness-of-fit techniques are essential for determining whether hypothetical models fit observed data. When at all reasonable, exact tests are preferred to either nonasymptotic or, especially, asymptotic tests. In addition, the structures of these tests yield entirely different detection capabilities for varying alternatives. A selection of techniques
Paul W. Mielke, Kenneth J. Berry
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Goodness-of-fit techniques are essential for determining whether hypothetical models fit observed data. When at all reasonable, exact tests are preferred to either nonasymptotic or, especially, asymptotic tests. In addition, the structures of these tests yield entirely different detection capabilities for varying alternatives. A selection of techniques
Paul W. Mielke, Kenneth J. Berry
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EDF goodness-of-fit tests based on centre-outward ordering
Journal of nonparametric statistics (Print), 2018The Kolmogorov-Smirnov (KS) test, the Cramér-von Mises (CvM) test, and the Anderson-Darling (AD) test are widely used goodness-of-fit tests based on the empirical distribution function (EDF).
Jun Yu Li
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Research of Statistic Distributions of Nonparametric Goodness-of-Fit Tests by Large Samples
IEEE International Conference on Actual Problems of Electronics Instrument Engineering, 2018The paper is devoted to application of nonparametric goodness-of-fit tests in the case of large samples. The main purpose is to develop the algorithm of application of nonparametric goodness-of- fit tests in the case of large samples.
M. Semenova, Dmitry S. Khalin
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2011
Goodness-of-fit tests are batteries of tests that test that the distribution of a sample is equal to some fixed-in-advance distribution. We already saw Q–Q plots in Chap. 5 where the samples were compared to some theoretical distributions but in a descriptive fashion, without formal inference.
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Goodness-of-fit tests are batteries of tests that test that the distribution of a sample is equal to some fixed-in-advance distribution. We already saw Q–Q plots in Chap. 5 where the samples were compared to some theoretical distributions but in a descriptive fashion, without formal inference.
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Goodness-of-fit tests for the Weibull distribution based on the Laplace transform and Stein’s method
Annals of the Institute of Statistical Mathematics, 2023B. Ebner +3 more
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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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A comparison of goodness-of-fit tests for the logistic regression model.
Statistics in Medicine, 1997D. Hosmer +3 more
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