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Book ChapterDOI

Individual Comparisons by Ranking Methods

Frank Wilcoxon
- 01 Dec 1945 - 
- Vol. 1, Iss: 6, pp 196-202
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TLDR
The comparison of two treatments generally falls into one of the following two categories: (a) a number of replications for each of the two treatments, which are unpaired, or (b) we may have a series of paired comparisons, some of which may be positive and some negative as mentioned in this paper.
Abstract
The comparison of two treatments generally falls into one of the following two categories: (a) we may have a number of replications for each of the two treatments, which are unpaired, or (b) we may have a number of paired comparisons leading to a series of differences, some of which may be positive and some negative. The appropriate methods for testing the significance of the differences of the means in these two cases are described in most of the textbooks on statistical methods.

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Journal ArticleDOI

Pelican Optimization Algorithm: A Novel Nature-Inspired Algorithm for Engineering Applications

Pavel Trojovský, +1 more
- 23 Jan 2022 - 
TL;DR: Simulation results and analysis show that Pelican Optimization Algorithm has a better and more competitive performance via striking a proportional balance between exploration and exploitation compared to eight competitor algorithms in providing optimal solutions for optimization problems.
Journal ArticleDOI

Protein disorder in the human diseasome: unfoldomics of human genetic diseases.

TL;DR: Observations provided the basis for the construction of the human-genetic-disease-associated unfoldome, and intrinsic disorder is common in proteins associated with many human genetic diseases.
Journal ArticleDOI

Near-Bayesian Support Vector Machines for imbalanced data classification with equal or unequal misclassification costs

TL;DR: A Near-Bayesian Support Vector Machine (NBSVM) is proposed for such imbalanced classification problems, by combining the philosophies of decision boundary shift and unequal regularization costs.
References
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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.
Book

The Design of Experiments

R. A. Fisher
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