Variance Function Estimation using SVM and Jack-Knife Method

• 발행기관: 한국자료분석학회
• 저자명: Jooyong Shim Daehyeon Cho
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.13 No.5, 2243~2251쪽, 전체 9쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2011.10.30

영문 초록

We propose an estimation method of variance functions in the heteroscedastic regression. To obtain more stable variance function estimation we use the averages of squared jack-knifed residuals obtained from support vector machine. We use the iteratively reweighted least squares procedure to estimate the regression function in support vector machine for easy model selection method which employs the generalized approximate cross validation techniques for choosing the hyper-parameters which affect the performance of the regression method. An artificial example and a real example are provided to indicate the usefulness of the proposed method for the estimation of variance functions.

목차

1. Introduction

2. Support Vector Machine

3. Variance function estimation by Jack-knife method

4. Numerical Studies

5. Conclusions

References

키워드

cross validation techniques heteroscedastic regression iteratively reweighted least squares procedure jack-knife method support vector machine