Titlebook: Comparing Distributions; Olivier Thas Book 2010 Springer-Verlag New York 2010 data analysis.goodness-of-fit tests.graphical methods.nonpar

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Smooth Testsroblem, though its applicability is very broad and often informative. Because many of the techniques and ideas used in this chapter rely heavily on what has been discussed in the previous chapters, this chapter is quite concise. The construction of the test is very similar to the one-sample smooth t

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Methods Based on the Empirical Distribution Functionto be compared. Such tests are generally known as .. The types of tests that are treated in this chapter are often of the same form of the EDF tests for the one-sample problem (Chapter 5). The Kolmogorov–Smirnov test is discussed in Section 11.1, and Section 11.2 concerns tests of the Anderson– Darl

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https://doi.org/10.1007/978-3-319-03686-1 respectively. In Section 5.3 we generate the class of EDF tests so that also more recent tests based on the empirical quantile function or the empirical characteristic function fit into the framework. We show that many of these tests are closely related to the class of smooth tests. Practical guidelines are provided in Section 5.6.

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Spain’s American Trade, 1708–1713Hilbert spaces, parameter estimation, and nonparametric density estimation. Some of the topics are quite technical, but we have tried to focus on the rationale and intuition behind them, rather than providing all the technical details

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Rosina Márquez Reiter,María Elena Placenciaility plots (PP and QQ) and comparison distributions are the topics of Sections 3.2 and 3.3, respectively. Both types of plots are related to very important goodness-of-fit tests, and we therefore spend quite some space on these methods

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