Inequality constrained quantile regression

Roger Koenker, Pin T Ng

Research output: Contribution to journalArticle

37 Citations (Scopus)

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 - 2005

Fingerprint

Quantile Regression
Regression Estimate
Interior-point Algorithm
Nonparametric Regression
Linear Constraints
Inequality Constraints
Sparsity
Linear algebra
MATLAB
Linear Inequalities
Computing
Quantile regression

Keywords

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

ASJC Scopus subject areas

  • Statistics and Probability

Cite this

Inequality constrained quantile regression. / Koenker, Roger; Ng, Pin T.

In: Sankhya: The Indian Journal of Statistics, Vol. 67, No. 2, 2005, p. 418-440.

Research output: Contribution to journalArticle

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