%0 Journal Article %1 Koenker.Ng2005 %A Koenker, Roger %A Ng, Pin %D 2005 %J Sankhyā: The Indian Journal of Statistics %K %N 2 %T Inequality Constrained Quantile Regression %U http://www.jstor.org/stable/25053440 %V 67 %X 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.

@article{Koenker.Ng2005, 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.}, added-at = {2012-04-25T15:15:13.000+0200}, author = {Koenker, Roger and Ng, Pin}, biburl = {https://www.bibsonomy.org/bibtex/26bc521394d0f6382a584d4c915eda1ac/marsianus}, interhash = {aebacad66a4660f46aee1356013f0ec1}, intrahash = {6bc521394d0f6382a584d4c915eda1ac}, journal = {Sankhyā: The Indian Journal of Statistics}, keywords = {}, number = 2, timestamp = {2012-04-25T15:15:13.000+0200}, title = {Inequality Constrained Quantile Regression}, url = {http://www.jstor.org/stable/25053440}, volume = 67, year = 2005 }