Modified Studentized Residual in Nonlinear Regression

• 발행기관: 한국자료분석학회
• 저자명: Myung-Wook Kahng
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.19 No.6, 2863~2869쪽, 전체 7쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2017.12.31

영문 초록

We discuss methods for detection of outlier in nonlinear regression. This issue is very essential in regression analysis because outlier can affect the covariate pattern and mislead regression interpretation. The existing literature on the identification of outlier in nonlinear regression is not as extensive as for linear regression. We propose a modification of studentized residual for testing outlier that overcomes many of the potential shortcomings of the ordinary studentized residual. The main idea of this article is to apply the linear approximation of nonlinear model function and consider the gradient as the design matrix. Also, we review leverage measures and explore the relationships between these leverage measures in nonlinear regression model. Example is given to illustrate the proposed methodology.

목차

1. Introduction

2. Leverages in Nonlinear Regression

3. Outlier Test

4. Examples

키워드

diagnostic intrinsic curvature leverage outlier studentized residual