Multivariate Permutation Tests
With Applications in Biostatistics
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Complex multivariate problems are frequently encountered in many scientific disciplines and it can be very difficult to obtain meaningful results. Permutation and nonparametric combination methods provide flexible solutions to complex problems by reducing the problem down to a set of simpler subproblems. The author presents a novel but well tested approach using real examples taken from biomedical research. Statistical analyses are performed in a nonparametric setting, so that no assumptions need be made about the underlying distribution and the dependence relations between variables. Provides...
Complex multivariate problems are frequently encountered in many scientific disciplines and it can be very difficult to obtain meaningful results. Permutation and nonparametric combination methods provide flexible solutions to complex problems by reducing the problem down to a set of simpler subproblems. The author presents a novel but well tested approach using real examples taken from biomedical research. Statistical analyses are performed in a nonparametric setting, so that no assumptions need be made about the underlying distribution and the dependence relations between variables. Provides a clear exposition of the use of multivariate permutation testing, with emphasis on the use of nonparametric combination methodology. Growing area of research with many practical applications, notably in biostatistics. Numerous case studies and examples help to illustrate the theory. Provides solutions to multi-aspect problems, to problems with missing data, analysis of factorial designs and repeated measures. Explains the analysis of categorical, ordered categorical, binary, continuous, and mixed variables in both an experimental and an observational context.Eine neuartige, aber sorgfältig getestete Methode des multivariaten Permutationtests kommt in diesem Band zur Sprache. Elegant unterteilt der Autor jedes Problem in seine Komponenten und wendet, wo es möglich ist, nichtparametrische Kombinationsverfahren an, um allgemeine Lösungen zu ermitteln. Der Vorteil dieser parameterfreien Ansätze liegt darin, daß keinerlei Annahmen bezüglich der Verteilung einer Population getroffen werden müssen. Viele Fallstudien und Beispiele illustrieren die Theorie. Die beiliegende CD enthält Software und Datensätze zum selbständigen Durcharbeiten der Beispiele.
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