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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국자료분석학회 Journal of The Korean Data Analysis Society Journal of The Korean Data Analysis Society 제19권 제6호
- 발행연도
- 2017.1
- 수록면
- 2,863 - 2,869 (7page)
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초록· 키워드
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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.
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참고문헌 (22)
참고문헌 신청
Bates, D. M. / 1980 / Relative curvature measures of nonlinearity (with discussion) / Journal of the Royal Statistical Society B 42 : 1 ~ 25
Bates, D. M. / 1981 / Parameter transformations for improved approximate confidence regions in nonlinear least squares / Annals of Statistics 9 : 1152 ~ 1167
Bates, D. M. / 1988 / Nonlinear Regression Analysis and Its Applications / John Wiley and Sons
Beckman, R. / 1983 / Outlier. . . s / Technometrics 25 : 119 ~ 149
Chatterjee, S. / 1986 / Influential observations, high leverage points, and outliers in linear regression / Statistical Science 1 : 379 ~ 416
Bates, D. M. / 1981 / Parameter transformations for improved approximate confidence regions in nonlinear least squares / Annals of Statistics 9 : 1152 ~ 1167
Bates, D. M. / 1988 / Nonlinear Regression Analysis and Its Applications / John Wiley and Sons
Beckman, R. / 1983 / Outlier. . . s / Technometrics 25 : 119 ~ 149
Chatterjee, S. / 1986 / Influential observations, high leverage points, and outliers in linear regression / Statistical Science 1 : 379 ~ 416
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