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1987
As before, let ξ1, ..., ξn be univariate independent random variables with the same continuous d.f. F Recall $${\text{D}}_{\text{n}} = \mathop {\sup }\limits_{\text{t}} |{\text{F}}_{\text{n}} \left( {\text{t}} \right) - {\text{F}}\left( {\text{t}} \right)| $$ , the Kolmogorov goodness of fit statistic.
Peter Gaenssler, Winfried Stute
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As before, let ξ1, ..., ξn be univariate independent random variables with the same continuous d.f. F Recall $${\text{D}}_{\text{n}} = \mathop {\sup }\limits_{\text{t}} |{\text{F}}_{\text{n}} \left( {\text{t}} \right) - {\text{F}}\left( {\text{t}} \right)| $$ , the Kolmogorov goodness of fit statistic.
Peter Gaenssler, Winfried Stute
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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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1999
Goodness-of-fit tests for continuos distributions are generally handled by the Kolmogorov-Smirnov test which in its classical form requires that the distribution is completely specified. In practice this is seldom the case and one then resorts to estimating parameters from the data and then examining Kolmogorov-Smirnov “type” tests. The standard tables
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Goodness-of-fit tests for continuos distributions are generally handled by the Kolmogorov-Smirnov test which in its classical form requires that the distribution is completely specified. In practice this is seldom the case and one then resorts to estimating parameters from the data and then examining Kolmogorov-Smirnov “type” tests. The standard tables
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Simulation, Estimation, and Goodness of Fit
2012Exploring and Relating Model to Data in Practice Previous chapters concentrated on the formulation and specification of exponential random graph models (ERGMs) for different types of relational data. In Chapter 6, we saw that effects represented by configurations and corresponding parameters define a distribution of graphs where the probability of ...
Koskinen, J., Snijders, T.A.B.
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1996
Abstract Throughout the previous chapters, we have seen various aspects of the model building process. In Chapter 3, we generally assumed that the chosen family of models, 𝒫, was suitable, and studied how to rank the relative merits of different members of that family, whether different functional forms or simply different parameter ...
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Abstract Throughout the previous chapters, we have seen various aspects of the model building process. In Chapter 3, we generally assumed that the chosen family of models, 𝒫, was suitable, and studied how to rank the relative merits of different members of that family, whether different functional forms or simply different parameter ...
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Communications in Statistics - Theory and Methods, 1999
This paper develops an approach to testing the adequacy of both classical and Bayesian models given sample data. An important feature of the approach is that we are able to test the practical scientific hypothesis of whether the true underlying model is close to some hypothesized model.
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This paper develops an approach to testing the adequacy of both classical and Bayesian models given sample data. An important feature of the approach is that we are able to test the practical scientific hypothesis of whether the true underlying model is close to some hypothesized model.
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Journal of the American Statistical Association, 1931
Edwin B. Wilson +2 more
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Edwin B. Wilson +2 more
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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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