References
- Basset, Jr. G. W.(1991). Equivariant, monotonic, 50% breakdown estimators, The American Statistician, Vol. 45, 135-137 https://doi.org/10.2307/2684377
- Belsely, D. A, Kuh, E. and Welsh, R E.(1980). Regression Diagnostics: lrifluential Data and Source of Collinearity. Wiley, New York
- Cook, R D. and Weisberg, S.(1980). Characterizations of an empirical influence function for detecting influential cases in regression, Technometrics, Vol. 22, 495-508 https://doi.org/10.2307/1268187
- Everitt, B. S.(1993). Cluster Analysis, Halsted Press, New York
- Hadi, A S. and Simonoff, J. S.(1993). Procedures for the identification of multiple outliers in linear models, journal of the American Statistical Association, Vol. 88, 1264-1272 https://doi.org/10.2307/2291266
- Hartigan, J. A(1975). Clustering Algorithms, Wiley, New York
- Hawkins, D. M., Bradu, D. and Kass, G. V.(1984). Location of several outliers in multiple regression data using elemental sets, Technometrics, Vol. 26, 197-208 https://doi.org/10.2307/1267545
- Kianifard, F. and Swallow, W. H.(1990). A Monte Carlo comparison of five procedures for identifying outliers in linear regression, Commun. Statist.-Theory Meth, Vol. 19, 1913-1938 https://doi.org/10.1080/03610929008830300
-
Kim, B. Y.(1996).
$ L_{\infty}$ -estimation based algorithm for the least median of squares estimator, The Korean Communications in Statistics, Vol. 3, 299-307 - Kim, B. Y. and Kim, H. Y(2002). A hybrid algorithm for identifying multiple outliers in linear regression, The Korean Communication in Statistics, Vol. 9, 291-304 https://doi.org/10.5351/CKSS.2002.9.1.291
- Marasinghe, M. G.(1985). A multistage procedure for detecting several outliers in linear regression, Technometrics, Vol. 27, 395-399 https://doi.org/10.2307/1270206
- Mojena, R(1977). Hierarchical grouping methods and stopping rules: an evaluation, Computer journal, Vol. 20, 359-363 https://doi.org/10.1093/comjnl/20.4.359
- Rousseeuw, P. J.(1984). Least median of squares regression, journal of the American Statistical Association, Vol. 79, 871-880 https://doi.org/10.2307/2288718
- Rousseeuw, P. J. and Leroy, A M.(1987). Robust Regression and Outlier Detection, Wiley-Interscience, New York
- Rousseeuw, P. J. and Zomeren, B. C.(1990). Unmasking multivariate outliers and leverage points, journal of the American Statistical Association, Vol. 85, 633-639 https://doi.org/10.2307/2289995
- Sebert, D. M., Montgomery, D. C. and RoIlier, D. A(1998). A clustering algorithm for identifying multiple outliers in linear regression, Computational Statistics & Data Analysis, Vol. 27, 461-484 https://doi.org/10.1016/S0167-9473(98)00021-8
Cited by
- A Criterion for the Selection of Principal Components in the Robust Principal Component Regression vol.18, pp.6, 2011, https://doi.org/10.5351/CKSS.2011.18.6.761