Estimating Structural Dimensionality using Contour Regression

• 발행기관: 한국자료분석학회
• 저자명: Pilkeun Choi Hakbae Lee Heemin Lee
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.14 No.2, 577~586쪽, 전체 10쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2012.04.30

영문 초록

Contour regression for estimating the central subspace is a method based on estimating contour directions of small variation in the response. These directions span the orthogonal complement of the central subspace and can be extracted according to two measures of variation in the response: simple and general contour regression (SCR and GCR). This paper investigates the estimation for structural dimensionality of the central space. We propose two kernel simple and general contour regression (KSCR and KGCR) to estimate structural dimensionality of the central space.

목차

1. Introduction

2. Dimension reduction subspace and central subspace

3. Contour regression

4. Simulation

5. Conclusion

References

키워드

Dimension Reduction Central Subspace Contour Regression Structural Dimensionality