Results 11 to 20 of about 4,684 (254)
Randomized goodness of fit tests [PDF]
Kybernetika, 2011 Summary: 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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, 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,
Renate L. E. P. Reniers (2622055) +1 more
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Goodness-of-fit tests for sparse nominal data based on grouping
Nonlinear Analysis, 2012 . For (very) sparse nominal data, common goodness-of-fit tests usually fail. Alternative goodness-of-fit tests based on extended empirical Bayes approach and grouping are proposed and their consistency is proved.
Marijus Radavičius, Pavel Samusenko
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Goodness-of-Fit Tests on Manifolds [PDF]
IEEE Transactions on Information Theory, 2021 We develop a general theory for the goodness-of-fit test to non-linear models. In particular, we assume that the observations are noisy samples of a submanifold defined by a \yao{sufficiently smooth non-linear map}. The observation noise is additive Gaussian.
Alexander Shapiro 0001 +2 more
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Tuning goodness-of-fit tests† [PDF]
Monthly Notices of the Royal Astronomical Society, 2019 10 pages, 11 ...
A Arrasmith, B Follin, E Anderes, L Knox
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KSD Aggregated Goodness-Of-Fit Test
Advances in Neural Information Processing Systems 35, 2022 We investigate properties of goodness-of-fit tests based on the Kernel Stein Discrepancy (KSD). We introduce a strategy to construct a test, called KSDAgg, which aggregates multiple tests with different kernels. KSDAgg avoids splitting the data to perform kernel selection (which leads to a loss in test power), and rather maximises the test power over a
Schrab, Antonin +2 more
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Composite Goodness-of-fit Tests with Kernels
J. Mach. Learn. Res., 2021 Model misspecification can create significant challenges for the implementation of probabilistic models, and this has led to development of a range of robust methods which directly account for this issue. However, whether these more involved methods are required will depend on whether the model is really misspecified, and there is a lack of generally ...
Oscar Key +3 more
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Goodness-of-Fit Tests in Nonparametric Regression [PDF]
SSRN Electronic Journal, 2006 AMS classifications: 62G08, 62G10, 62G20, 62G30; 60F17.
Einmahl, J.H.J., Keilegom, I. van
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Distribution free goodness-of-fit tests for linear processes [PDF]
, 2004 This article proposes a class of goodness-of-fit tests for the autocorrelation function of a time series process, including those exhibiting long-range dependence.
Velasco Gómez, Carlos +8 more
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Cramer-von Mises and Anderson-Darling goodness of fit tests for extreme value distributions with unknown parameters [PDF]
, 2004 The use of goodness of fit tests based on Cramer-von Mises and Anderson-Darling statistics is discussed, with reference to the composite hypothesis that a sample of observations comes from a distribution, FH, whose parameters are unspecified.
Laio, Francesco, Francesco Laio
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