Bootstrap confidence bands and partial linear quantile regression

Song Song*, Ya'acov Ritov, Wolfgang K. Härdle

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

In this paper bootstrap confidence bands are constructed for nonparametric quantile estimates of regression functions, where resampling is done from a suitably estimated empirical distribution function (edf) for residuals. It is known that the approximation error for the confidence band by the asymptotic Gumbel distribution is logarithmically slow. It is proved that the bootstrap approximation provides an improvement. The case of multidimensional and discrete regressor variables is dealt with using a partial linear model. An economic application considers the labor market differential effect with respect to different education levels.

Original languageEnglish
Pages (from-to)244-262
Number of pages19
JournalJournal of Multivariate Analysis
Volume107
DOIs
StatePublished - May 2012

Keywords

  • Bootstrap
  • Confidence bands
  • Kernel smoothing
  • Nonparametric fitting
  • Partial linear model
  • Quantile regression

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