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Titlebook: Goodness-of-Fit Tests and Model Validity; C. Huber-Carol,N. Balakrishnan,M. Mesbah Book 2002 Birkhäuser Boston 2002 Estimator.Extension.Pl

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Approximate ModelsThe increasing size and complexity of data sets increasingly forces us to deal with less than perfect, but ever more complicated models. I shall discuss general issues of model fitting and of assessing the quality of fit, and the important and often crucial roles of robustness and simulation.
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A CLT for the L_2 Norm of the Regression Estimators Under α-Mixing: Application to G-O-F TestsWe establish a central limit theorem for integrated square error of least squares splines estimators based on α-mixing. The new theorem is used to study the behavior of an asymptotic goodness-of-fit test.
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Brian Moeran,Timothy de Waal Malefyttical application of our software to treat the data of Milliken and the data of Daniel. The power of the test for testing normality against the family of logistic distributions, formed on the Neyman-Pearson classes, is also studied.
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Statistical Tests for Normal Family in Presence of Outlying Observationstical application of our software to treat the data of Milliken and the data of Daniel. The power of the test for testing normality against the family of logistic distributions, formed on the Neyman-Pearson classes, is also studied.
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Goodness-of-Fit Statistics for the Exponential Distribution When the Data are Grouped990), Kolmogrov-Smirnov type statistics are developed with the maximum likelihood estimator of the scale parameter substituted for the true unknown scale. The asymptotic theory for both the statistics is studied and power studies carried out via simulations.
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Testing the Goodness-of-Fit of a Linear Model in Nonparametric Regressione test. Even if the procedure is based on large sample behaviour, simulation experiments reveal that, for small samples, the proposed statistic is close to the asymptotic distribution and the test has good power properties.
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