An interior point algorithm for nonlinear quantile regression

A new algorithm for computing quantile regression estimates for problems in which the response function is nonlinear in parameters is described. The nonlinear l 1 estimation problem is a special (median) case. The algorithm is closely related to recent developments on interior point methods for solv...

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Bibliographic Details
Published in:Journal of econometrics 1996-03, Vol.71 (1), p.265-283
Main Authors: Koenker, Roger, Park, Beum J.
Format: Article
Language:English
Subjects:
Algorithms
Econometrics
Interior point algorithms
Linear programming
Nonlinear programming
Nonlinear regression
Quantile regression
Regression analysis
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Description
Summary:A new algorithm for computing quantile regression estimates for problems in which the response function is nonlinear in parameters is described. The nonlinear l 1 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.
ISSN:0304-4076
1872-6895
DOI:10.1016/0304-4076(96)84507-6