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http://dx.doi.org/10.5351/KJAS.2013.26.6.915

Stepwise Estimation for Multiple Non-Crossing Quantile Regression using Kernel Constraints  

Bang, Sungwan (Department of Mathematics, Korea Military Academy)
Jhun, Myoungshic (Department of Statistics, Korea University)
Cho, HyungJun (Department of Statistics, Korea University)
Publication Information
The Korean Journal of Applied Statistics / v.26, no.6, 2013 , pp. 915-922 More about this Journal
Abstract
Quantile regression can estimate multiple conditional quantile functions of the response, and as a result, it provide comprehensive information of the relationship between the response and the predictors. However, when estimating several conditional quantile functions separately, two or more estimated quantile functions may cross or overlap and consequently violate the basic properties of quantiles. In this paper, we propose a new stepwise method to estimate multiple non-crossing quantile functions using constraints on the kernel coefficients. A simulation study are presented to demonstrate satisfactory performance of the proposed method.
Keywords
Kernel; multiple quantile regression; non-crossing; quadratic programming;
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