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

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...

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Bibliographic Details
Published in:Journal of the American Statistical Association 2007-06, Vol.102 (478), p.686-694
Main Authors: Sen, Pranab K, Tsai, Ming-Tien, Jou, Yuh-Shan
Format: Article
Language:English
Subjects:
Applications
Biodiversity
Biostatistics
Data models
Entropy
Estimators
Exact sciences and technology
General topics
Genetic variation
Genetics
Genomes
Genomics
Hamming distance
Hamming distances
Marginal approach
Marginality
Mathematical models
Mathematics
Nuisance functional contours
Null hypothesis
P values
Parametric inference
Probability and statistics
Quantitative analysis
Resampling plan
Ribonucleic acid
RNA
Sample surveys
Sampling
Sampling theory, sample surveys
Sciences and techniques of general use
Severe acute respiratory syndrome
Statistical analysis
Statistical methods
Statistical variance
Statistics
Theory and Methods
Unbiased estimators
Union-intersection principle
Variance
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Summary: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.
ISSN:0162-1459
1537-274X
DOI:10.1198/016214507000000077