Bias reduction for nonparametric correlation coefficients under the bivariate normal copula assumption with known detection limits

Lei Nie, Haitao Chu, Valeriy R. Korostyshevskiy

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

The authors derive the asymptotic mean and bias of Kendall's tau and Spearman's rho in the presence of left censoring in the bivariate Gaussian copula model. They show that tie corrections for left-censoring brings the value of these coefficients closer to zero. They also present a bias reduction method and illustrate it through two applications.

Original languageEnglish (US)
Pages (from-to)427-442
Number of pages16
JournalCanadian Journal of Statistics
Volume36
Issue number3
DOIs
StatePublished - Sep 2008

Keywords

  • Bias reduction
  • Bivariate gaussian copula mode
  • Bivariate normal distribution
  • Detection limit
  • Kendall's tau
  • Left censoring
  • Pearson's correlation coefficient
  • Spearman's rho

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