Article
MSC:
62G10,
62G99,
62H20,
62P10,
62P99,
92C40,
92D10 | MR 2411125 | Zbl 1195.62097 | DOI: 10.1007/s10492-008-0005-1
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Keywords:
dimensional asymptotics; genomics; multiple hypotheses testing; microarray data model; nonparametrics; U-statistics
dimensional asymptotics; genomics; multiple hypotheses testing; microarray data model; nonparametrics; U-statistics
Summary:
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 stressed.
References:
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