Asymptotic Efficiency of a Class of C-Sample Tests (Classic Reprint) - Softcover

Inhaltsangabe

Explore the power and reach of nonparametric tests across multiple samples. This rigorous work extends the theory of rank-based tests to the c-sample problem, revealing how a single framework unifies classic methods like Kruskal-Wallis and Freund-Ansari with modern score tests.

In clear, accessible terms, the book develops conditions for joint asymptotic normality of test statistics, derives limiting distributions under varied alternatives, and shows how these tests relate to and generalize well-known procedures. It balances theoretical depth with practical insight, highlighting when and how the c-sample approach achieves efficiency comparable to traditional parametric tests.

• Connections between classic two-sample tests and their multi-sample counterparts.


• A detailed treatment of test statistics built from ranks and order statistics.


• Conditions that guarantee asymptotic normality and the resulting limiting behavior under alternatives.


• Guidance on applying the theory to translate alternatives and assess power.

Ideal for readers with a foundation in statistics who want a deeper, math-grounded understanding of nonparametric methods for comparing multiple populations.

Ideal for researchers and advanced students of statistics seeking a rigorous treatment of nonparametric multi-sample testing and its practical implications.

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