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Article
The Two Sample Problem for Multiple Categorical Variables: With Application to HIV-1 Genotype Evolution
The International Journal of Biostatistics (2007)
  • A. G. DiRienzo, Harvard University
Abstract

Comparing two large multivariate distributions is potentially complicated at least for the following reasons. First, some variable/level combinations may have a redundant difference in prevalence between groups in the sense that the difference can be completely explained in terms of lower-order combinations. Second, the total number of variable/level combinations to compare between groups is very large, and likely computationally prohibitive. In this paper, for both the paired and independent sample case, an approximate comparison method is proposed, along with a computationally efficient algorithm, that estimates the set of variable/level combinations that have a non-redundant different prevalence between two populations. The probability that the estimate contains one or more false or redundant differences is asymptotically bounded above by any pre-specified level for arbitrary data-generating distributions. The method is shown to perform well for finite samples in a simulation study, and is used to investigate HIV-1 genotype evolution in a recent AIDS clinical trial.

Keywords
  • conditional independence,
  • family-wise error rate,
  • genomics,
  • genotype sequence,
  • HIV-1 genotype evolution,
  • multiple hypothesis testing,
  • simultaneous inference
Publication Date
May, 2007
Citation Information
A. G. DiRienzo. "The Two Sample Problem for Multiple Categorical Variables: With Application to HIV-1 Genotype Evolution" The International Journal of Biostatistics Vol. 2 Iss. 1 (2007)
Available at: http://works.bepress.com/greg_dirienzo/3/