Partially Linear Support Vector Quantile Regression Using Asymmetric e-insensitive Loss Function

• 발행기관: 한국자료분석학회
• 저자명: Jooyong Shim Kyungha Seok Changha Hwang
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.15 No.4, 1767~1776쪽, 전체 10쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2013.08.30

영문 초록

Quantile regression has been an popular method for estimating the quantiles of a conditional distribution on the values of covariates. Support vector quantile regression (SVQR) is capable of providing a good description of the linear and nonlinear relationships among random variables. The SVQR results in nonsparsity because of the zeroinsensitiveness of check function. In this paper, we propose a partially linear SVQR (PSVQR) using an asymmetric e-insensitive loss function in SVQR in order to provide the efficient estimation and sparsity. The proposed loss function is designed to provide more sparsity by adjusting insensitiveness according the sign of residuals. The asymmetric e-insensitive loss function is used to increase the sparsity efficiently. We have a generalized approximate cross validation function for choosing hyperparameters which affects the performance of the proposed estimator. Experimental results are then presented which illustrate the performance of the proposed method by comparing with nonsparse partially linearly SVQR. Through the examples we showed that the proposed method provides the sparsity and better performance than partially linear support vector quantile regression using check function.

목차

1. Introduction

2. Partially linear SVQR

3. Sparse partially linear SVQR

4. Numerical studies

5. Conclusions

References

키워드

Asymmetric e-insensitive loss function model selection partially linear quantile regression sparsity support vector