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Goodness-of-Fit Tests on a Circle. II

Biometrika, 1961
Abstract : 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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A test of goodness of fit

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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A test of goodness of fit

Statistica Neerlandica, 1966
info:eu-repo/semantics ...
Vandewiele, Georges, De Witte, Paul
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»Smooth test» for goodness of fit

Scandinavian Actuarial Journal, 1937
Abstract 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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Goodness-of-Fit Tests

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

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

2023
N. Balakrishnan   +2 more
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Goodness of Fit and Related Inference Processes for Quantile Regression

Journal of the American Statistical Association, 1999
Roger Koenker
exaly   +2 more sources

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