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High-dimensional data models, often with low sample size, abound in manyinterdisciplinary studies, genomics and large biological systems being mostnoteworthy. The conventional assumption of multinormality or linearity ofregression may not be plausible for such models which are likely to bestatistically complex due to a large number of parameters as well as variousunderlying restraints. As such, parametric approaches may not be veryeffective. Anything beyond parametrics, albeit, having increased scope androbustness perspectives, may generally be baffled by the low sample size andhence unable to give reasonable margins of errors. Kendall's tau statistic isexploited in this context with emphasis on dimensional rather than sample sizeasymptotics. The Chen--Stein theorem has been thoroughly appraised in thisstudy. Applications of these findings in some microarray data models areillustrated.

Kendall's tau in high-dimensional genomic parsimony