http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Multi-sample functional statistical data analysis
11 pages, 1 unnumbered pages : illustrations ; 28 cm
This paper discusses a functional approach to the problem of comparison of multi-samples (two samples or c samples, where c> or = 2). The data consists of c random samples whose probability distributions are to be tested for equality. A diversity of statistics to test equality of c samples are presented in a unified framework with the aim of helping the researcher choose the optimal procedures which provide greatest insight about how the samples differ in their distributions. Concepts discussed are: sample distribution functions; ranks; mid-distribution function; two- sample t test and nonparametric Wilcoxon test; multi-sample analysis of variance and Kruskal Wallis test; Anderson Darling and Cramer von Mises tests; components and linear rank statistics; comparison distribution and comparison density functions, especially for discrete distributions; components with orthogonal polynomial score functions; chi-square tests and their components.
"Texas A&M Research Foundation Project Number 5641."
Includes bibliographical references (page 10).
Sponsored by the U.S. Army Research Office DAAL03-87-K-0003
Technical report (Texas A & M University. Department of Statistics) ; no. 54