Geodesic
Kendall's tau-type rank statistics in genome data
Applications of Mathematics, Tome 53 (2008) no. 3, pp. 207-221

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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.
DOI : 10.1007/s10492-008-0005-1
Classification : 62G10, 62G99, 62H20, 62P10, 62P99, 92C40, 92D10
Keywords: dimensional asymptotics; genomics; multiple hypotheses testing; microarray data model; nonparametrics; U-statistics 
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     title = {Kendall's tau-type rank statistics in genome data},
     journal = {Applications of Mathematics},
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     zbl = {1195.62097},
     language = {en},
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Kang, Moonsu; Sen, Pranab K. Kendall's tau-type rank statistics in genome data. Applications of Mathematics, Tome 53 (2008) no. 3, pp. 207-221. doi: 10.1007/s10492-008-0005-1

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