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Inequality Constrained Quantile Regression

Roger Koenker and Pin Ng
Sankhyā: The Indian Journal of Statistics (2003-2007)
Vol. 67, No. 2, Quantile Regression and Related Methods (May, 2005), pp. 418-440
Published by: Springer on behalf of the Indian Statistical Institute
Stable URL: http://www.jstor.org/stable/25053440
Page Count: 23
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Inequality Constrained Quantile Regression
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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.

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