Influence diagnostics in semiparametric regression models

Statistics and Probability Letters

Volumn 60, Issue 1, 2002, Pages 49-58

  Kim, Choongrak ^a   Park, Byeong U ^b   Kim, Woochul ^b  

^a Pusan National University   (South Korea)

^b Seoul National University   (South Korea)

Author keywords

Bandwidth; Cross validation; Hat matrix; Influential observations; Local polynomial; Smoothing spline

Indexed keywords

EID: 0036838401     PISSN: 01677152     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-7152(02)00268-7     Document Type: Article

References (19)

1. Atkinson, A.C., 1981. Two graphical displays for outlying and influential observation in regression. Biometrika 68, 13-20.
2. Belsley, D.A., Kuh, E., Welsch, R.E., 1980. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. Wiley, New York.
3. Cook, R.D., 1977. Detection of influential observation in linear regression. Technometrics 19, 15-18.
4. Cook, R.D., Weisberg, S., 1982. Residuals and Influence in Regression. Chapman & Hall, New York.
5. Eubank, R.L., 1985. Diagnostics for smoothing splines. J. Roy. Statist. Soc. Ser. B 47, 332-341.
6. Eubank, R.L., 1988. Spline Smoothing and Nonparametric Regression. Marcel Dekker, New York.
7. Fung, W.K., Zhu, Z.Y., Wei, B.C., He, X., 2002. Influence diagnostics and outlier tests for semiparametric mixed models. J. Roy. Statist. Soc. Ser. B, to appear.
8. Green, P.J., Silverman, B.W., 1994. Nonparametric Regression and Generalized Liner Models. Chapman & Hall, London.
9. Hastie, T.J., Tibshirani, R.J., 1990. Generalized Additive Models. Chapman & Hall, New York.
10. Kim, C., 1996. Cook's distance in spline smoothing. Statist. Probab. Lett. 31, 139-144.
11. Kim, C., Kim, W., 1998. Some diagnostics results in nonparametric density estimation. Comm. Statist. Theory Methods 27, 291-303.
12. Kim, C., Storer, B.E., 1996. Reference values for Cook's distance. Comm. Statist. Simulation Comput. 25, 691-709.
13. Kim, C., Lee, Y., Park, B.U., 2001. Cook's distance in local polynomial regression. Statist. Probab. Lett. 54, 33-40.
14. Severini, T.A., Staniswalis, J.G., 1994. Quasi-likelihood estimation in semiparametric models. J. Amer. Statist. Assoc. 89, 501-511.
15. Silverman, B.W., 1985. Some aspects of the spline smoothing approach to non-parametric regression curve fitting (with discussion). J. Roy. Statist. Soc. Ser. B 47, 1-52.
16. Sockett, E.B., Daneman, D., Clarson, C., Ehrich, R.M., 1987. Factors affecting the patterns of residual insulin secretion during the 6rst year of type I (insulin dependent) diabetes mellitus in children. Diabetes 30, 453-459.
17. Speckman, P.E., 1988. Regression analysis for partially linear models. J. Roy. Statist. Soc. Ser. B 50, 413-436.
18. Thomas, W., 1991. Influence diagnostics for the cross-validated smoothing parameter in spline smoothing. J. Amer. Statist. Assoc. 86, 693-698.
19. Zhang, D., Lin, X., Raz, J., Sower, M.F., 1998. Semiparametric stochastic mixed models for longitudinal data. J. Amer. Statist. Assoc. 93, 710-719.