On a robust local estimator for the scale function in heteroscedastic nonparametric regression

When the data used to fit an heteroscedastic nonparametric regression model are contaminated with outliers, robust estimators of the scale function are needed in order to obtain robust estimators of the regression function and to construct robust confidence bands. In this paper, local M-estimators o...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autor principal: Boente, G.
Otros Autores: Ruiz, M., Zamar, R.H
Formato: Capítulo de libro
Lenguaje:Inglés
Publicado: 2010
Acceso en línea:Registro en Scopus
DOI
Handle
Registro en la Biblioteca Digital
Aporte de:Registro referencial: Solicitar el recurso aquí
LEADER 09076caa a22008177a 4500
001 PAPER-7718
003 AR-BaUEN
005 20230518203729.0
008 190411s2010 xx ||||fo|||| 00| 0 eng|d
024 7 |2 scopus  |a 2-s2.0-77955054100 
040 |a Scopus  |b spa  |c AR-BaUEN  |d AR-BaUEN 
030 |a SPLTD 
100 1 |a Boente, G. 
245 1 3 |a On a robust local estimator for the scale function in heteroscedastic nonparametric regression 
260 |c 2010 
270 1 0 |m Boente, G.; Instituto de Cálculo, Ciudad Universitaria, Pabellón 2, Buenos Aires, C1428EHA, Argentina; email: gboente@dm.uba.ar 
506 |2 openaire  |e Política editorial 
504 |a Aït Sahalia, Y., (1995), The delta method for nonlinear kernel functionals. Ph.D. dissertation, University of Chicago; Beaton, A., Tukey, J., The fitting of power series, meaning polynomials, illustrated on band-spectroscopic data (1974) Technometrics, 16, pp. 147-185 
504 |a Boente, G., Fraiman, R., Robust nonparametric regression estimation for dependent observations (1989) Annals of Statistics, 17, pp. 1242-1256 
504 |a Boente, G., Fraiman, R., A functional approach to robust nonparametric regression (1991) Directions in Robust Statistics and Diagnostics, , Springer, Berlin, Heidelberg 
504 |a Boente, G., Fraiman, R., Meloche, J., Robust plug-in bandwidth estimators in nonparametric regression (1997) Journal of Statistical Planning and Inference, 57, pp. 109-142 
504 |a Boente, G., Ruiz, M., Zamar, R., (2009), http://www.ic.fcen.uba.ar/preprints/boente_ruiz_zamar_TR.pdf, Robust estimation of the scale function in nonparametric regression models. Available at: ; Boente, G., Rodriguez, D., Robust bandwidth selection in semiparametric partly linear regression models: Monte Carlo study and influential analysis (2008) Computational Statistics & Data Analysis, 52, pp. 2808-2828 
504 |a Bosq, D., (1996) Nonparametric Statistics for Stochastic Processes. Estimation and Prediction, , Springer-Verlag, New York 
504 |a Brown, L., Levine, M., Variance estimation in nonparametric regression via the difference sequence method (2007) Annals of Statistics, 35, pp. 2219-2232 
504 |a Caliskan, D., Croux, C., Gelper, S., Efficient and robust scale estimation for trended time series (2009) Statistics and Probability Letters, 79, pp. 1900-1905 
504 |a Cantoni, E., Ronchetti, E., Resistant selection of the smoothing parameter for smoothing splines (2001) Statistics and Computing, 11, pp. 141-146 
504 |a Dette, H., A consistent test for heteroscedasticity in nonparametric regression based on the kernel method (2002) Journal of Statistical Planning and Inference, 103, pp. 311-329 
504 |a Dette, H., Hetzler, B., A simple test for the parametric form of the variance function in nonparametric regression (2009) The Annals of the Institute of Statistical Mathematics, 61, pp. 861-886 
504 |a Dette, H., Munk, A., Wagner, T., Estimating the variance in nonparametric regression - What is a reasonable choice? (1998) Journal of the Royal Statistics Society, Series B, 60, pp. 751-764 
504 |a Gasser, T., Müller, H.G., Estimating regression functions and their derivatives by the kernel method (1984) Scandinavian Journal of Statistics, 11, pp. 171-185 
504 |a Gasser, T., Sroka, L., Jennen-Steinmetz, C., Residual variance and residual pattern in nonlinear regression (1986) Biometrika, 73, pp. 625-633 
504 |a Gelper, S., Schettlinger, K., Croux, C., Gather, U., Robust online scale estimation in time series: a regression-free approach (2009) Journal of Statistical Planning and Inference, 139, pp. 335-339 
504 |a Georgiev, A., Consistent nonparametric multiple regression: The fixed design case (1989) Journal of Multivariate Analysis, 25, pp. 100-110 
504 |a Ghement, I., Ruiz, M., Zamar, R., Robust estimation of error scale in nonparametric regression models (2008) Journal of Statistical Planning and Inference, 138, pp. 3200-3216 
504 |a Giloni, A., Simonoff, J., The conditional breakdown properties of least absolute value local polynomial estimators (2005) Journal of Nonparametric Statistics, 17, pp. 15-30 
504 |a Hall, P., Kay, J., Titterington, D., Asymptotically optimal difference-based estimation of variance in nonparametric regression (1990) Biometrika, 77, pp. 521-528 
504 |a Hannig, J., Lee, T., Robust SiZer for exploration of regression structures and outlier detection (2006) Journal of Computational and Graphical Statistics, 15, pp. 101-117 
504 |a Härdle, W., Gasser, T., Robust nonparametric function fitting (1984) Journal of the Royal Statistical Society, Series B, 46, pp. 42-51 
504 |a Härdle, W., Tsybakov, A., Robust nonparametric regression with simultaneous scale curve estimation (1988) Annals of Statistics, 25, pp. 443-456 
504 |a Leung, D., Cross-validation in nonparametric regression with outliers (2005) Annals of Statistics, 33, pp. 2291-2310 
504 |a Leung, D., Marriott, F., Wu, E., Bandwidth selection in robust smoothing (1993) Journal of Nonparametric Statistics, 2, pp. 333-339 
504 |a Levine, M., (2003), Variance estimation for nonparametric regression and its applications. Ph.D. Dissertation, University of Pennsylvania; Levine, M., Bandwidth selection for a class of difference-based variance estimators in the nonparametric regression: a possible approach (2006) Computational Statistics & Data Analysis, 50, pp. 3405-3431 
504 |a Manchester, L., Empirical Influence for robust smoothing (1996) Australian & New Zealand Journal of Statistics, 38, pp. 275-296 
504 |a Maronna, R., Martin, D., Yohai, V., (2006) Robust Statistics: Theory and Methods, , John Wiley & Sons 
504 |a Müller, H., Stadtmüller, U., Estimation of heteroscedasticity in regression analysis (1987) Annals of Statistics, 15, pp. 610-625 
504 |a Pelligrad, M., Utev, S., Bandwidth choice for nonparametric regression (1997) Annals of Statistics, 25, pp. 443-456 
504 |a Rice, J., Bandwidth choice for nonparametric regression (1984) Annals of Statistics, 12, pp. 1215-1230 
504 |a Rousseeuw, P., Croux, C., Alternatives to the median absolute deviation (1993) Journal of the American Statististical Association, 88, pp. 1273-1283 
504 |a Rousseeuw, P., Hubert, M., Regression-free and robust estimation of scale for bivariate data (1996) Computational Statistics & Data Analysis, 21, pp. 67-85 
504 |a Ruiz, M., (2008), Contribución a la Teoría de la Estimación Robusta de Escala en Modelos de Regresión Noparamétricos. Ph.D. Dissertation; Ruppert, D., Wand, M., Holst, U., Hössjer, Local polynomial variance-function estimation (1997) Technometrics, 39, pp. 262-273 
504 |a Tamine, J., (2002), Smoothed influence function: another view at robust nonparametric regression. Discussion paper 62, Sonderforschungsbereich 373, Humboldt-Universität zu Berlin; Tukey, J., (1977) Exploratory Data Analysis, , Addison-Wesley, Reading, MA 
504 |a Ullah, A., Specification analysis of econometric models (1985) Journal of quantitative economics, 2, pp. 187-209 
520 3 |a When the data used to fit an heteroscedastic nonparametric regression model are contaminated with outliers, robust estimators of the scale function are needed in order to obtain robust estimators of the regression function and to construct robust confidence bands. In this paper, local M-estimators of the scale function based on consecutive differences of the responses, for fixed designs are considered. Under mild regularity conditions, the asymptotic behavior of the local M-estimators for general weight functions is derived. © 2010 Elsevier B.V.  |l eng 
593 |a Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Argentina 
593 |a CONICET, Argentina 
593 |a Universidad Nacional de Río IV, Argentina 
593 |a University of British Columbia, Canada 
690 1 0 |a HETEROSCEDASTICITY 
690 1 0 |a LOCAL M-ESTIMATORS 
690 1 0 |a NONPARAMETRIC REGRESSION 
690 1 0 |a ROBUST ESTIMATION 
700 1 |a Ruiz, M. 
700 1 |a Zamar, R.H. 
773 0 |d 2010  |g v. 80  |h pp. 1185-1195  |k n. 15-16  |p Stat. Probab. Lett.  |x 01677152  |w (AR-BaUEN)CENRE-6916  |t Statistics and Probability Letters 
856 4 1 |u https://www.scopus.com/inward/record.uri?eid=2-s2.0-77955054100&doi=10.1016%2fj.spl.2010.03.015&partnerID=40&md5=c5816df08390a3c96ba8347e96c632af  |y Registro en Scopus 
856 4 0 |u https://doi.org/10.1016/j.spl.2010.03.015  |y DOI 
856 4 0 |u https://hdl.handle.net/20.500.12110/paper_01677152_v80_n15-16_p1185_Boente  |y Handle 
856 4 0 |u https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01677152_v80_n15-16_p1185_Boente  |y Registro en la Biblioteca Digital 
961 |a paper_01677152_v80_n15-16_p1185_Boente  |b paper  |c PE 
962 |a info:eu-repo/semantics/article  |a info:ar-repo/semantics/artículo  |b info:eu-repo/semantics/publishedVersion 
999 |c 68671