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Statistische Hefte, 1967
The authors propose a new test of goodness of fit for the simple null hypothesis that the actual distribution is equal to a given, everywhere continuous distribution function. Under theNeyman-Pearson setup they obtain a test which (a) is meaningful without reference to any specific set of alternatives, and (b) is based on the fact we tend to dis ...
Kale, B. K., Godambe, V. P.
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The authors propose a new test of goodness of fit for the simple null hypothesis that the actual distribution is equal to a given, everywhere continuous distribution function. Under theNeyman-Pearson setup they obtain a test which (a) is meaningful without reference to any specific set of alternatives, and (b) is based on the fact we tend to dis ...
Kale, B. K., Godambe, V. P.
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»Smooth test» for goodness of fit
Scandinavian Actuarial Journal, 1937Abstract Dedicated to the memory of Karl Pearson (27 March 1857—27 April 1936) who originated the problem of a test for goodness of fit and was first to advance its solution.
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Randomized goodness of fit tests
Kybernetika, 2011Summary: Classical goodness-of-fit tests are no longer asymptotically distributional free if parameters are estimated. For a parametric model and the maximum likelihood estimator the empirical processes with estimated parameters is asymptotically transformed into a time transformed Brownian bridge by adding an independent Gaussian process that is ...
Friedrich Liese, Bing Liu
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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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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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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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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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The Multidimensional Kolmogorov Goodness-of-Fit Test
Biometrika, 1980Saunders, Roy, Laud, Purushottam
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Goodness of Fit and Related Inference Processes for Quantile Regression
Journal of the American Statistical Association, 1999Roger Koenker
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