High-Dimension, Low-Sample Size Perspectives in Constrained Statistical Inference: The SARSCoV RNA Genome in Illustration

Abstract

High-dimensional categorical data models, often, with inadequately large sample sizes, crop up in many fields of application. The SARS epidemic, originating in Southern China in 2002, had an identified single-stranded and positive-sense RNA virus with large genome size and moderate mutation rate. This genomic study is used as a prime illustration for motivating appropriate statistical methodology for comprehending the genomic variation in such high dimensional categorical data models. Because of underlying restraints, a pseudo-marginal approach based on Hamming distance is considered in a constrained statistical inference setup.The union-intersection principle and jackknifing methods are incorporated in exploring appropriate statistical procedures.