High-dimension, low-sample size perspectives in constrained statistical inference: The SARSCoV RNA genome in illustration

• 成果类型: Article
• 署名作者: Sen, Pranab K.; Tsai, Ming-Tien; Jou, Yuh-Shan
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214507000000077
• 发表日期: 2007
• 页码: 686-694
• 关键词: coronavirus; diversity; epidemiology; SEQUENCES

摘要

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. The present 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 pseudomarginal 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.