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A Simulation Study of Goodness-of-Fit Tests for Binary Regression with Applications to Norwegian Intensive Care Registry Data

, 2019
Ellisif Nygaard
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Smooth Tests of Goodness of Fit

Technometrics, 1991
AbstractSmooth 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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Length tests for goodnesss-of-fit

Biometrika, 1991
Consider an i.i.d. sample X 1,..., X n with distribution function F, which throughout is assumed to be twice continuously differentiable with support [0,1] and strictly positive derivative on [0,1]. Denote by $$0={X_{0:n}}\leqslant {X_{1:n}}\leqslant\cdots\leqslant{X_{n:n}}\leqslant{X_{n+1:n}}=1$$ (1) the order statistics, and the spacings by
Reschenhofer, Erhard, Bomze, Immanuel
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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

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

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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