Graphical display in outlier diagnostics; adequacy and robustness

Nethal K. Jjo

Ayuda

Graphical display in outlier diagnostics; adequacy and robustness

Autores: Nethal K. Jjo
Localización: Sort: Statistics and Operations Research Transactions, ISSN 1696-2281, Vol. 29, Nº. 1, 2005, págs. 1-9
Idioma: inglés
Títulos paralelos:
- Representación gráfica en diagnóstico de valores atípicos; adecuación y robustez.
Enlaces
- Texto completo (pdf)
Resumen
- Outlier robust diagnostics (graphically) using Robustly Studentized Robust Residuals (RSRR) and Partial Robustly Studentized Robust Residuals (PRSRR) are established. One problem with some robust residual plots is that the residuals retain information from certain predicated values (Velilla, 1998). The RSRR and PRSRR techniques are unaffected by this complication and as a result they provide more interpretable results.
Referencias bibliográficas
- Atkinson, A.C. (1985). Plots, Transformation, and Regression. An introduction to graphical methods of diagnostic regression analysis. New...
- Barnett, V. and Lewis, T. (1994). Outliers in Statistical Data. 3rd edition. New York: John Wiley.
- Belsley, D.A., Kuh, E. and Welsch, R.E. (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. New York:...
- Coakley, C. and Hettmansperger, T.P. (1993). A bounded influence, high break-down efficient regression estimator. Journal American Statistical...
- Carroll, R.J. and Ruppert, D. (1985). Transformation in regression: a robust analysis. Technometrics, 27, 1-12.
- Cook, R.D., and Weisberg, S. (1982). Residuals and Influence in Regression. New York: Chapman and Hall.
- Cook, R.D., Hawkins, D.M. and Weisberg, S. (1992). Comparison of model misspecification diagnostics using residuals from least median of squares...
- Chatterjee, S. and Hadi, A.S. (1988). Sensitivity Analysis in Linear Regression. New York: John Wiley.
- du Toit, S.H.C., Steyn, A.G.W. and Stumpf, R.H. (1986). Graphical Exploratory Data Analysis. New York: Springer-Verlag.
- Hocking, R.R (1996). Method and Application of Linear Models. New York: John Wiley & Sons. Inc.
- Hocking, R.R. and Pendleton, O.J. (1983). The regression dilemma. Communication in Statistics, part A-Theory and Methods, 67, 388-394.
- Huber, P. J.(1973). Robust Regression: asymptotic, conjectures, and Monte Carlo. Annals of Statistics, 1, 799-821.
- Hussain, S.S. and Sprent, P. (1983). Non-Parametric Regression. Journal of the Royal Statistical Society Series A. Part 2, 146, 182-191.
- Jajo, N. K. (1999). Robust diagnostics of outliers in linear regression, Ph.D. Thesis. Nanaki University, Tianjin, China.
- Jajo, N.K. and Wu, X.Z. (1998). Robust diagnostics of outliers in multiple linear regression. Proceedings of joint Statistical conference....
- Kianifard, F. and Swallow, W.H. (1989). Using recursive residuals, calculated on adaptive ordered observations, to identify outliers in linear...
- Kianifard, F. and Swallow, W.H. (1996). A review of the development and application of recursive residuals in linear models. Journal American...
- Mckean, J.W., Sheather, S.J. and Hettmansperger, T.P. (1993). The use and interpretation of residuals based on robust estimation. Journal...
- Mckean, J.W., Sheather, S.J. and Hettmansperger, T.P. (1994). Robust and high-breakdown fits of polynomial models. Technometrics, 36, 409-415.
- Mickey, M.R., Dunn, O.J. and Clark, V. (1967). Note on use of stepwise regression in detecting outliers. Computer & Biomed. Res., 1, 105-111.
- Rousseeuw, P.J. and Leroy, A.M. (1987). Robust Regression and Outlier Detection. New York: Wiley.
- Ruppert, D. and Carroll, R.J. (1980). Trimmed least squares estimation in the linear regression model. Journal American Statistical Association,...
- Simpson, D.G., Ruppert, D. and Carroll, R. (1992). On one-step GM estimates and stability of influences in linear regression. Journal American...
- Swallow, H.W. and Kianifard, F. (1996). Using a robust scale estimates in detecting multiple outliers in linear regression. Biometrics, 52,...
- Velilla, S. (1998). A note on the behavior of residual plots in regression. Statistics & Probability Letters, 37, 269-278.