Kendall’s tau-type rank statistics in genome data |
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Authors: | Moonsu Kang Pranab K Sen |
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Institution: | (1) Department of Biostatistics, Statistics and Operations Research, University of North Carolina, Chapel Hill, NC 27599-7420, USA |
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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 stressed. |
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Keywords: | dimensional asymptotics genomics multiple hypotheses testing microarray data model nonparametrics U-statistics |
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