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Validity and Reliability of the Turkish Version of the Celiac Dietary Adherence Test. [PDF]
Medicina (Kaunas)Serin Y +7 more
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A reducing demand for tertiary hospital-based gender affirming care in Victoria, Australia. [PDF]
Intern Med JLum S +6 more
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The wrapping Epanechnikov exponential distribution: A novel flexible model for asymmetric circular data. [PDF]
PLoS OneAlotaibi SAS +3 more
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An evaluation of outpatient satisfaction in Chinese tertiary hospitals from a patient-centered perspective: a cross-sectional study. [PDF]
Front Public HealthWei Y +8 more
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Improved Goodness-Of-Fit Tests
Biometrika, 1971Two statistics for testing goodness of fit for small sample sizes are provided. The first statistic, S, can be used to test the fit to any completely specified continuous distribution function and is more powerful than the Kolmogorov-Smirnov statistic in the cases tested.
Finkelstein, J. M., Schafer, R. E.
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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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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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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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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.
openaire +1 more source