Efficient evaluation of ranking procedures when the number of units is large, with application to SNP identification

Research output: Contribution to journalArticlepeer-review

Abstract

Simulation-based assessment is a popular and frequently necessary approach for evaluating statistical procedures. Sometimes overlooked is the ability to take advantage of underlying mathematical relations and we focus on this aspect. We show how to take advantage of large-sample theory when conducting a simulation using the analysis of genomic data as a motivating example. The approach uses convergence results to provide an approximation to smaller-sample results, results that are available only by simulation. We consider evaluating and comparing various ranking-based methods for identifying the most highly associated SNPs in a genome-wide association study, derive integral equation representations of the pre-posterior distribution of percentiles produced by three ranking methods, and provide examples comparing performance. These results are of interest in their own right and set the framework for a more extensive set of comparisons.

Original languageEnglish (US)
Pages (from-to)34-49
Number of pages16
JournalBiometrical Journal
Volume52
Issue number1
DOIs
StatePublished - Feb 2010

Keywords

  • Efficient simulation
  • Ranking procedures
  • SNP identification

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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