Journal ArticleDOI
Approximations of the critical region of the fbietkan statistic
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In this paper, the authors compare two new approximations with the usual x2 and F large sample approximings for the one-way Kruskal-Wallis test statistic.Abstract:
The Friedman (1937) test for the randomized complete block design is used to test the hypothesis of no treatment effect among k treatments with b blocks. Difficulty in determination of the size of the critical region for this hypothesis is com¬pounded by the facts that (1) the most recent extension of exact tables for the distribution of the test statistic by Odeh (1977) go up only to the case with k6 and b6, and (2) the usual chi-square approximation is grossly inaccurate for most commonly used combinations of (k,b). The purpose of this paper 2 is to compare two new approximations with the usual x2 and F large sample approximations. This work represents an extension to the two-way layout of work done earlier by the authors for the one-way Kruskal-Wallis test statistic.read more
Citations
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Journal Article
Statistical Comparisons of Classifiers over Multiple Data Sets
TL;DR: A set of simple, yet safe and robust non-parametric tests for statistical comparisons of classifiers is recommended: the Wilcoxon signed ranks test for comparison of two classifiers and the Friedman test with the corresponding post-hoc tests for comparisons of more classifiers over multiple data sets.
Journal ArticleDOI
A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms
TL;DR: The basics are discussed and a survey of a complete set of nonparametric procedures developed to perform both pairwise and multiple comparisons, for multi-problem analysis are given.
Journal ArticleDOI
Rank Transformations as a Bridge between Parametric and Nonparametric Statistics
TL;DR: Rank as mentioned in this paper is a nonparametric procedure that is applied to the ranks of the data instead of to the data themselves, and it can be viewed as a useful tool for developing non-parametric procedures to solve new problems.
Proceedings Article
KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework
Jesús Alcalá-Fdez,Alberto Fernández,Julián Luengo,Joaquín Derrac,Salvador García,Luciano Sánchez,Francisco Herrera +6 more
TL;DR: The aim of this paper is to present three new aspects of KEEL: KEEL-dataset, a data set repository which includes the data set partitions in theKEELformat and some guidelines for including new algorithms in KEEL, helping the researcher to compare the results of many approaches already included within the KEEL software.
Journal ArticleDOI
Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power
TL;DR: This paper focuses on the use of nonparametric statistical inference for analyzing the results obtained in an experiment design in the field of computational intelligence, and presents a case study which involves a set of techniques in classification tasks.
References
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Book
Practical Nonparametric Statistics
TL;DR: Probability Theory. Statistical Inference. Contingency Tables. Appendix Tables. Answers to Odd-Numbered Exercises and Answers to Answers to Answer Questions as discussed by the authors.
Book
Nonparametric Statistical Methods
Myles Hollander,Douglas A. Wolfe +1 more
TL;DR: An ideal text for an upper-level undergraduate or first-year graduate course, Nonparametric Statistical Methods, Second Edition is also an invaluable source for professionals who want to keep abreast of the latest developments within this dynamic branch of modern statistics.
Journal ArticleDOI
The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance
TL;DR: The use of ranks to avoid the assumption of normality implicit in the analysis of variance has been studied in this article, where the use of rank to avoid normality is discussed.
Journal ArticleDOI
Synthesis of variance.
TL;DR: The distribution of a linear combination of two statistics distributed as is Chi-square is studied in this paper, and it is concluded that the approximation is sufficiently accurate to use in many practical applications.