Open Access
Applications of Mathematics
Moonsu Kang,Pranab Kumar Sen +1 more
TLDR
In this article, Kendall's tau-type rank statistics are employed for statistical inference, avoiding most of parametric assumptions to a greater extent, and the proposed procedures are compared with Kendall’s tau statistic based ones for microarray data models.Abstract:
High-dimensional data models abound in genomics studies, where often inadequately small sample sizes create impasses for incorporation of standard statistical tools Conventional assumptions of linearity of regression, homoscedasticity and (multi-) normality of errors may not be tenable in many such interdisciplinary setups In this study, Kendall’s tau-type rank statistics are employed for statistical inference, avoiding most of parametric assumptions to a greater extent The proposed procedures are compared with Kendall’s tau statistic based ones Applications in microarray data models are stressedread more
References
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Controlling the false discovery rate: a practical and powerful approach to multiple testing
Yoav Benjamini,Yosef Hochberg +1 more
TL;DR: In this paper, a different approach to problems of multiple significance testing is presented, which calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate, which is equivalent to the FWER when all hypotheses are true but is smaller otherwise.
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Estimates of the Regression Coefficient Based on Kendall's Tau
TL;DR: In this article, a simple and robust estimator of regression coefficient β based on Kendall's rank correlation tau is studied, where the point estimator is the median of the set of slopes (Yj - Yi )/(tj-ti ) joining pairs of points with ti ≠ ti.
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A new measure of rank correlation
TL;DR: Rank correlation as mentioned in this paper is a measure of similarity between two rankings of the same set of individuals, and it has been used in psychological work to compare two different rankings of individuals in order to indicate similarity of taste.
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A direct approach to false discovery rates
TL;DR: The calculation of the q‐value is discussed, the pFDR analogue of the p‐value, which eliminates the need to set the error rate beforehand as is traditionally done, and can yield an increase of over eight times in power compared with the Benjamini–Hochberg FDR method.
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Poisson Approximation for Dependent Trials
TL;DR: In this paper, a general method of obtaining and bounding the error in approximating the distribution of the dependent Bernoulli random variables by the Poisson distribution is presented, which is similar to that of Charles Stein (1970) in his paper on normal approximation for dependent random variables.