Inequality constrained quantile regression

Roger Koenker, Pin Ng

Research output: Contribution to journalArticle

39 Scopus citations

Abstract

An algorithm for computing parametric linear quantile regression estimates subject to linear inequality constraints is described. The algorithm is a variant of the interior point algorithm described in Koenker and Portnoy (1997) for unconstrained quantile regression and is consequently quite efficient even for large problems, particularly when the inherent sparsity of the resulting linear algebra is exploited. Applications to qualitatively constrained nonparametric regression are described in the penultimate sections. Implementations of the algorithm are available in MATLAB and R.

Original languageEnglish (US)
Pages (from-to)418-440
Number of pages23
JournalSankhya: The Indian Journal of Statistics
Volume67
Issue number2
StatePublished - Nov 30 2005

Keywords

  • Interior point algorithm
  • Qualitative constraints
  • Quantile regression
  • Smoothing
  • Sparse matrices

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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