Abstract
In this article, we introduce an algorithm to compute the regression quantile functions. This algorithm combines three algorithms — the simplex, interior point, and smoothing algorithm. The simplex and interior point algorithms come from the background of linear programming (Portnoy and Koenker, 1997). While the simplex method can handle small to middle sized data sets, the interior point method can handle large to huge data sets efficiently. The smoothing algorithm is specially designed for the L 1 or quantile regression type of problems (Chen, 2002), and it outperforms the other two algorithms for fat data sets. Combining these three algorithms produces an algorithm, which is adaptive in the sense that it can intelligently detect the input data sets and select one of the three algorithms to efficiently compute the regression quantile functions.
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© 2004 Springer Basel AG
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Chen, C. (2004). An Adaptive Algorithm for Quantile Regression. In: Hubert, M., Pison, G., Struyf, A., Van Aelst, S. (eds) Theory and Applications of Recent Robust Methods. Statistics for Industry and Technology. Birkhäuser, Basel. https://doi.org/10.1007/978-3-0348-7958-3_4
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DOI: https://doi.org/10.1007/978-3-0348-7958-3_4
Publisher Name: Birkhäuser, Basel
Print ISBN: 978-3-0348-9636-8
Online ISBN: 978-3-0348-7958-3
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