Penalized quantile regression tree
- Authors
- Kim, Jaeoh; Cho, HyungJun; Bang, Sungwan
- Issue Date
- Dec-2016
- Publisher
- KOREAN STATISTICAL SOC
- Keywords
- decision tree; penalized regression; quantile regression
- Citation
- KOREAN JOURNAL OF APPLIED STATISTICS, v.29, no.7, pp.1361 - 1371
- Indexed
- KCI
- Journal Title
- KOREAN JOURNAL OF APPLIED STATISTICS
- Volume
- 29
- Number
- 7
- Start Page
- 1361
- End Page
- 1371
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/86745
- DOI
- 10.5351/KJAS.2016.29.7.1361
- ISSN
- 1225-066X
- 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.
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