An interior point algorithm for nonlinear quantile regression

Roger Koenker, Beum J. Park

Research output: Contribution to journalArticlepeer-review

Abstract

A new algorithm for computing quantile regression estimates for problems in which the response function is nonlinear in parameters is described. The nonlinear l1 estimation problem is a special (median) case. The algorithm is closely related to recent developments on interior point methods for solving linear programs. Performance of the algorithm on a variety of test problems including the censored linear quantile regression problem of Powell (1986) is reported.

Original languageEnglish (US)
Pages (from-to)265-283
Number of pages19
JournalJournal of Econometrics
Volume71
Issue number1-2
DOIs
StatePublished - 1996

Keywords

  • Interior point algorithms
  • Linear programming
  • Nonlinear programming
  • Nonlinear regression
  • Quantile regression

ASJC Scopus subject areas

  • Economics and Econometrics

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