{"id":647,"date":"2017-09-28T07:05:05","date_gmt":"2017-09-28T07:05:05","guid":{"rendered":"http:\/\/jsr.isrt.ac.bd\/?post_type=article&p=647"},"modified":"2017-09-28T08:03:14","modified_gmt":"2017-09-28T08:03:14","slug":"nonparametric-tests-ordered-diversity-genomic-sequence","status":"publish","type":"article","link":"http:\/\/jsr.isrt.ac.bd\/article\/nonparametric-tests-ordered-diversity-genomic-sequence\/","title":{"rendered":"Nonparametric tests for ordered diversity in a genomic sequence"},"content":{"rendered":"
In genomics (SNP and RNA amino acid studies), typically, we encounter enormously
\nlarge dimensional qualitative categorical data models without an ordering
\nof the categories, thus preempting the use of conventional measures of dispersion
\n(variation or diversity) as well as other measures which assume some latent trait
\nvariable(s). The Gini-Simpson diversity measure, often advocated for diversity
\nanalysis in one-dimensional models, has been adapted to formulate measures of
\ndiversity and co-diversity based on the Hamming distance in the multidimensional
\nsetup. Based on certain (molecular) biologically interpretable monotone diversity
\nperspectives, an ordering of the Gini-Simpson measures across the genome (positions)
\nis formulated in a meaningful way. Motivated by this feature, nonparametric
\ninference for such ordered measures is considered here, and their applications
\nstressed.<\/p>\n