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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Book 2010istician who is involved in comparative studies....The book gives a self-contained theoretical treatment of a wide range of goodness-of-fit methods, including graphical methods, hypothesis tests, model selection and density estimation. It relies on parametric, semiparametric and nonparametric theory

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Smooth Testsved some optimality properties. Although smooth tests are considered as nonparametric tests, they are actually constructed by first considering a .-dimensional .in which the hypothesised distribution is embedded. These smooth alternatives are the subject of Section 4.1. The tests are given in Sectio

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Methods Based on the Empirical Distribution Functione old tests, as the Kolmogorov-Smirnov test, but also in recent years new tests have still been added to this class. A discussion on the EDF and empirical processes has been given in Sections 2.1 and 2.2. Sections 5.1 and 5.2 are devoted to the Kolmogorov-Smirnov and the Cramér-von Mises type tests,

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Introductionre the distribution of the sample observations with a hypothesised distribution, we are now concerned with comparing the distributions of two or more populations from which we have observations at our disposal. As both classes of problems are about comparing distributions, many of the methods develo

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Preliminaries (Building Blocks)esumes that the null hypothesis specifies some parameterised parametric distribution. On the other hand, most two-sample tests can be based on an asymptotic null distribution that can be derived from a central limit theorem, or from the application of the continuous mapping theorem and the weak conv

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