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http://dx.doi.org/10.5351/KJAS.2016.29.7.1361

Penalized quantile regression tree  

Kim, Jaeoh (Department of Statistics, Korea University)
Cho, HyungJun (Department of Statistics, Korea University)
Bang, Sungwan (Department of Mathematics, Korea Military Academy)
Publication Information
The Korean Journal of Applied Statistics / v.29, no.7, 2016 , pp. 1361-1371 More about this Journal
Abstract
Quantile regression provides a variety of useful statistical information to examine how covariates influence the conditional quantile functions of a response variable. However, traditional quantile regression (which assume a linear model) is not appropriate when the relationship between the response and the covariates is a nonlinear. It is also necessary to conduct variable selection for high dimensional data or strongly correlated covariates. In this paper, we propose a penalized quantile regression tree model. The split rule of the proposed method is based on residual analysis, which has a negligible bias to select a split variable and reasonable computational cost. A simulation study and real data analysis are presented to demonstrate the satisfactory performance and usefulness of the proposed method.
Keywords
decision tree; penalized regression; quantile regression;
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