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http://dx.doi.org/10.7465/jkdi.2017.28.3.533

Multivariate 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
Journal of the Korean Data and Information Science Society / v.28, no.3, 2017 , pp. 533-545 More about this Journal
Abstract
Quantile regression models provide a variety of useful statistical information by estimating the conditional quantile function of the response variable. However, the traditional linear quantile regression model can lead to the distorted and incorrect results when analysing real data having a nonlinear relationship between the explanatory variables and the response variables. Furthermore, as the complexity of the data increases, it is required to analyse multiple response variables simultaneously with more sophisticated interpretations. For such reasons, we propose a multivariate quantile regression tree model. In this paper, a new split variable selection algorithm is suggested for a multivariate regression tree model. This algorithm can select the split variable more accurately than the previous method without significant selection bias. We investigate the performance of our proposed method with both simulation and real data studies.
Keywords
Data mining; multivariate data analysis; quantile regression; regression tree;
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Times Cited By KSCI : 2  (Citation Analysis)
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