论文信息 - Diagnostics for nonlinear L p

Diagnostics for nonlinear L p

Abstract In this paper we propose diagnostics for nonlinear Lp-norm estimation. The basic idea is to replace the notion of likelihood displacement (Cook and Weisberg, 1982; Cook, 1986) by that of Lp-norm displacement. The general methods and diagnostics developed by Cook (1986) and Schall and Dunne (1988a, b, 1989) for the case of maximum likelihood estimation can then be extended to Lp-norm estimation.

Robert Schall | René Gonin | R. Schall | R. Gonin

[1] A. Money,et al. Nonlinear Lp-Norm Estimation , 2020 .

[2] G. Stewart. Collinearity and Least Squares Regression , 1987 .

[3] J. A. John,et al. Influential Observations and Outliers in Regression , 1981 .

[4] S. Weisberg,et al. Residuals and Influence in Regression , 1982 .

[5] Christopher J. Nachtsheim,et al. Diagnostics for mixed-model analysis of variance , 1987 .

[6] Tim Dunne,et al. A note on the relationship between parameter collinearity and local influence , 1992 .

[7] R. Welsch,et al. The Hat Matrix in Regression and ANOVA , 1978 .

[8] R. Gonin. Numerical algorithms for solving nonlinear l -norm estimation problems: Part i - a first-order gradient algorithm for well-conditioned small residual problems , 1986 .

[9] D. Pregibon. Logistic Regression Diagnostics , 1981 .

[10] S. D. Toit,et al. Numerical algorithms for solving nonlinear L р-norm estimation problems: part II - a mixture method for large residual and illo-conditioned problems , 1987 .