Sample size determination for high dimensional parameter estimation with application to biomarker identification

Binyan Jiang, Jialiang Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

We consider sample size calculation to obtain sufficient estimation precision and control the length of confidence intervals under high dimensional assumptions. In particular, we intend to provide more general results for sample size determination when a large number of parameter values need to be computed for a fixed sample. We consider three design approaches: normal approximation, inequality method and regression method. These approaches are applied to sample size calculation in estimating the Net Reclassification Improvement (NRI) and the Integrated Discrimination Improvement (IDI) for a diagnostic or screening study. Two medical examples are also provided as illustration. Our results suggest the regression method in general can yield a much smaller sample size than other methods.
Original languageEnglish
Pages (from-to)54-65
Number of pages12
JournalComputational Statistics and Data Analysis
Volume118
DOIs
Publication statusPublished - 1 Feb 2018

Keywords

  • Bernstein inequality
  • Bonferroni inequality
  • IDI
  • NRI
  • Sample size calculation
  • Training sample

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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