Kendall's tau-type rank statistics in genome data

Moonsu Kang; Pranab Kumar Sen

Applications of Mathematics (2008)

  • Volume: 53, Issue: 3, page 207-221
  • ISSN: 0862-7940

Abstract

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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.

How to cite

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Kang, Moonsu, and Sen, Pranab Kumar. "Kendall's tau-type rank statistics in genome data." Applications of Mathematics 53.3 (2008): 207-221. <http://eudml.org/doc/37779>.

@article{Kang2008,
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.},
author = {Kang, Moonsu, Sen, Pranab Kumar},
journal = {Applications of Mathematics},
keywords = {dimensional asymptotics; genomics; multiple hypotheses testing; microarray data model; nonparametrics; U-statistics; dimensional asymptotics; genomics; multiple hypotheses testing; microarray data model; U-statistics},
language = {eng},
number = {3},
pages = {207-221},
publisher = {Institute of Mathematics, Academy of Sciences of the Czech Republic},
title = {Kendall's tau-type rank statistics in genome data},
url = {http://eudml.org/doc/37779},
volume = {53},
year = {2008},
}

TY - JOUR
AU - Kang, Moonsu
AU - Sen, Pranab Kumar
TI - Kendall's tau-type rank statistics in genome data
JO - Applications of Mathematics
PY - 2008
PB - Institute of Mathematics, Academy of Sciences of the Czech Republic
VL - 53
IS - 3
SP - 207
EP - 221
AB - 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.
LA - eng
KW - dimensional asymptotics; genomics; multiple hypotheses testing; microarray data model; nonparametrics; U-statistics; dimensional asymptotics; genomics; multiple hypotheses testing; microarray data model; U-statistics
UR - http://eudml.org/doc/37779
ER -

References

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