The choice of the initial estimate for computing MM-estimates

We show, using a Monte Carlo study, that MM-estimates with projection estimates as starting point of an iterative weighted least squares algorithm, behave more robustly than MM-estimates starting at an S-estimate and similar Gaussian efficiency. Moreover the former have a robustness behavior close t...

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Autor principal: Svarc, M.
Otros Autores: Yohai, V.J, et al.; Fundacao para a Ciencia e a Tecnologia (FCT); Instituto Nacional de Estatistica; PORTO Camara Municipal; PSE; Universidade do Porto - Faculdade de Engenharia (FEUP)
Formato: Acta de conferencia Capítulo de libro
Lenguaje:Inglés
Publicado: springer berlin 2008
Acceso en línea:Registro en Scopus
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100 1 |a Svarc, M. 
245 1 4 |a The choice of the initial estimate for computing MM-estimates 
260 |b springer berlin  |c 2008 
506 |2 openaire  |e Política editorial 
504 |a Davies, L., The asymptotics of S-estimators in the linear regression model (1990) The Annals of Statistics, 18, pp. 1651-1675 
504 |a Donoho, D.L., Huber, P.J., The notion of breakdown-point (1983) A Festschrift for Erich L. Lehmann, pp. 157-184. , P.J. Bickel, K.A. Doksum and J.L Hodges, Jr. (Eds.) Wadsworth, Belmont, California 
504 |a Hampel, F.R., A general qualitative definition of robustness (1971) The Annals of Mathematical Statistics, 42, pp. 1887-1896 
504 |a Hossjer, O., On the optimality of S-estimators (1992) Statistics & Probability Letters, 14, pp. 413-419 
504 |a Maronna, R.A., Yohai, V.J., Bias-robust estimates of regression based on projections (1993) The Annals of Statistics, 21, pp. 965-990 
504 |a Maronna, R.A., Martin, R.D., Yohai, V.J., (2006) Robust Statistics: Theory and Methods, , Wiley, Chichister 
504 |a Martin, R.D., Yohai, V.J., Zamar, R., Min-max bias robust regression (1989) The Annals of Statistics, 17, pp. 1608-1630 
504 |a Rousseeuw, P.J., Least median of squares regression (1984) J. Am. Stat. Assoc., 79, pp. 871-880 
504 |a Rousseeuw, P.J., Yohai, V.J., Robust regression by means of sestimators (1984) Robust and Nonlinear Time Series, Lecture Notes in Statistics, 26, pp. 256-272. , J. Franke, W. Hardle and R.D. Martin (Eds) Springer-Verlag, Berlin 
504 |a Salibian-Barrera, M., Yohai, V.J., A fast algorithm for sregression estimates (2006) Journal of Computational and Graphical Statistics, 15, pp. 414-427 
504 |a Yohai, V.J., High breakdown point and high efficiency robust estimates for regression (1987) The Annals of Statistics, 15, pp. 642-656A4 - et al.; Fundacao para a Ciencia e a Tecnologia (FCT); Instituto Nacional de Estatistica; PORTO Camara Municipal; PSE; Universidade do Porto - Faculdade de Engenharia (FEUP) 
520 3 |a We show, using a Monte Carlo study, that MM-estimates with projection estimates as starting point of an iterative weighted least squares algorithm, behave more robustly than MM-estimates starting at an S-estimate and similar Gaussian efficiency. Moreover the former have a robustness behavior close to the P-estimates with an additional advantage: they are asymptotically normal making statistical inference possible.  |l eng 
593 |a Departamento de Matemática y Ciencias, Universidad de San Andrés, Vito Dumas 284, 1644 Victoria, Pcia. de Buenos Aires, Argentina 
593 |a Departamento de Matemática, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria Pabellón 1, 1426 Buenos Aires, Argentina 
690 1 0 |a P-ESTIMATES 
690 1 0 |a ROBUST REGRESSION 
690 1 0 |a S-ESTIMATES 
690 1 0 |a GAUSSIANS 
690 1 0 |a INITIAL ESTIMATE 
690 1 0 |a ITERATIVE-WEIGHTED 
690 1 0 |a P-ESTIMATES 
690 1 0 |a ROBUST REGRESSIONS 
690 1 0 |a S-ESTIMATES 
690 1 0 |a STATISTICAL INFERENCE 
690 1 0 |a ITERATIVE METHODS 
700 1 |a Yohai, V.J. 
700 1 |a et al.; Fundacao para a Ciencia e a Tecnologia (FCT); Instituto Nacional de Estatistica; PORTO Camara Municipal; PSE; Universidade do Porto - Faculdade de Engenharia (FEUP) 
711 2 |c Porto  |d 24 August 2008 through 29 August 2008  |g Código de la conferencia: 106188 
773 0 |d springer berlin, 2008  |h pp. 503-515  |p COMPSTAT - Proc. Comput. Stat., Symp.  |n COMPSTAT 2008 - Proceedings in Computational Statistics, 18th Symposium  |z 9783790820836  |t 18th Symposium on Computational Statistics, COMPSTAT 2008 
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