While preserving the clear, accessible style of previous editions, this volume reflects the latest developments in computer-intensive methods that deal with intractable analytical problems and unwieldy data sets.
While preserving the clear, accessible style of previous editions, this volume reflects the latest developments in computer-intensive methods that deal with intractable analytical problems and unwieldy data sets.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Some basic concepts. Fundamentals of nonparametric methods. Location inference for single samples. Other single-sample inferences. Methods for paired samples. Methods for two independent samples. Basic tests for three or more samples. Analysis of structured data. Analysis of survival data. Correlation and concordance. Bivariate linear regression. Categorical data. Association in categorical data. Robust estimation. Modern nonparametrics.
1 Introducing nonparametric methods.- 1.1 Basic statistics.- 1.2 Hypothesis tests.- 1.3 Estimation.- 1.4 Samples and populations.- 1.5 Further reading.- 1.6 Computers and nonparametric methods.- Exercises.- 2 Location estimates for single samples.- 2.1 The sign test.- 2.2 Inferences about medians based on ranks.- 2.3 Other location estimators.- 2.4 Fields of application.- Exercises.- 3 Distribution tests and rank transformations for single samples.- 3.1 Matching samples to distributions.- 3.2 Robustness.- 3.3 Transformations of ranks.- 3.4 Practical implications of efficiency.- 3.5 Modified assumptions.- 3.6 Fields of application.- Exercises.- 4 Methods for paired samples.- 4.1 Comparisons in pairs.- 4.2 A less obvious use of the sign test.- 4.3 Fields of application.- Exercises.- 5 Tests and estimation for two independent samples.- 5.1 Location tests and estimates.- 5.2 Wilcoxon-Mann-Whitney confidence intervals.- 5.3 Tests on functions of ranks.- 5.4 Tests for equality of variance.- 5.5 A test for a common distribution.- 5.6 Fields of application.- Exercises.- 6 Three or more samples.- 6.1 Possible extensions.- 6.2 Location tests for independent samples.- 6.3 Tests for heterogeneity of variance for independent samples.- 6.4 Further tests for several independent samples.- 6.5 Location comparisons for related samples.- 6.6 Fields of application.- Exercises.- 7 Bivariate and multivariate data.- 7.1 Correlation in bivariate data.- 7.2 Nonparametric bivariate linear regression.- 7.3 Monotonie regression.- 7.4 Multivariate data.- 7.5 Fields of application.- Exercises.- 8 Counts and categories.- 8.1 Categorical data.- 8.2 Tests for independence in two-way tables.- 8.3 The log-linear model.- 8.4 Goodness of fit tests for discrete data.- 8.5 Fields of application.-Exercises.- 9 Robustness, jackknives and bootstraps.- 9.1 The computer and robustness.- 9.2 Jackknives and bootstraps.- 9.3 Fields of application.- Exercises.- 10 Looking ahead.- 10.1 Nonparametric methods in a wider context.- 10.2 Developments from basic techniques.- 10.3 More sophisticated developments.- 10.4 The Bayesian approach.- A1 Random variables.- A2 Permutations and combinations.- A6 Least squares regression.- A7 Data sets.- A8 Tables of critical values for nonparametric methods.- References.- Solutions to odd-numbered exercises.
Some basic concepts. Fundamentals of nonparametric methods. Location inference for single samples. Other single-sample inferences. Methods for paired samples. Methods for two independent samples. Basic tests for three or more samples. Analysis of structured data. Analysis of survival data. Correlation and concordance. Bivariate linear regression. Categorical data. Association in categorical data. Robust estimation. Modern nonparametrics.
1 Introducing nonparametric methods.- 1.1 Basic statistics.- 1.2 Hypothesis tests.- 1.3 Estimation.- 1.4 Samples and populations.- 1.5 Further reading.- 1.6 Computers and nonparametric methods.- Exercises.- 2 Location estimates for single samples.- 2.1 The sign test.- 2.2 Inferences about medians based on ranks.- 2.3 Other location estimators.- 2.4 Fields of application.- Exercises.- 3 Distribution tests and rank transformations for single samples.- 3.1 Matching samples to distributions.- 3.2 Robustness.- 3.3 Transformations of ranks.- 3.4 Practical implications of efficiency.- 3.5 Modified assumptions.- 3.6 Fields of application.- Exercises.- 4 Methods for paired samples.- 4.1 Comparisons in pairs.- 4.2 A less obvious use of the sign test.- 4.3 Fields of application.- Exercises.- 5 Tests and estimation for two independent samples.- 5.1 Location tests and estimates.- 5.2 Wilcoxon-Mann-Whitney confidence intervals.- 5.3 Tests on functions of ranks.- 5.4 Tests for equality of variance.- 5.5 A test for a common distribution.- 5.6 Fields of application.- Exercises.- 6 Three or more samples.- 6.1 Possible extensions.- 6.2 Location tests for independent samples.- 6.3 Tests for heterogeneity of variance for independent samples.- 6.4 Further tests for several independent samples.- 6.5 Location comparisons for related samples.- 6.6 Fields of application.- Exercises.- 7 Bivariate and multivariate data.- 7.1 Correlation in bivariate data.- 7.2 Nonparametric bivariate linear regression.- 7.3 Monotonie regression.- 7.4 Multivariate data.- 7.5 Fields of application.- Exercises.- 8 Counts and categories.- 8.1 Categorical data.- 8.2 Tests for independence in two-way tables.- 8.3 The log-linear model.- 8.4 Goodness of fit tests for discrete data.- 8.5 Fields of application.-Exercises.- 9 Robustness, jackknives and bootstraps.- 9.1 The computer and robustness.- 9.2 Jackknives and bootstraps.- 9.3 Fields of application.- Exercises.- 10 Looking ahead.- 10.1 Nonparametric methods in a wider context.- 10.2 Developments from basic techniques.- 10.3 More sophisticated developments.- 10.4 The Bayesian approach.- A1 Random variables.- A2 Permutations and combinations.- A6 Least squares regression.- A7 Data sets.- A8 Tables of critical values for nonparametric methods.- References.- Solutions to odd-numbered exercises.
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