Connecting Partial Least Squares Regression and Generalized Ridge Regression

• 발행기관: 한국자료분석학회
• 저자명: 김종덕(Jong-Duk Kim) 문승호(Sungho Moon)
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.7 No.1, 61~71쪽, 전체 11쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2005.02.28

영문 초록

The main purpose of this paper is to make a connection between two regression methods which seemingly appear to be quite different: partial least squares regression (PLSR) and generalized ridge regression (GRR). The former uses latent factors extracted from both and and appears as an iterative algorithm in most literatures, and the latter adds small quantities to the diagonal of prior to inversion to reduce the variance of estimated regression coefficients. We connect the two methods to develop a new regression solution. This can be done by modifying PLSR expression in an approximate form and relating it to GRR expression, which results in a new type of estimates of ridge parameters. An example is given to illustrate the above method and its predictive ability is compared with other common regression methods.

목차

1. Introduction

2. Expressions for GRR and PLSR Solutions

3. Connecting PLSR and GRR

4. An Example

5. Summary

References

키워드

partial least squares regression generalized ridge regression ridge constants ridge regression partial least squares-generalized ridge regression