• Robust bandwidth selectors in semiparametric partly linear regression models

RODRIGUEZ, DANIELA; BOENTE, GRACIELA

Beijing, China

Congreso; International Conference on Robust Statistics (ICORS 2004); 2004

It is well known that, both in linear regression and in nonparametric regression, least squares estimators can be seriously affected by anomalous data. The same statement holds for partly linear models. To avoid that problem, Bianco and Boente (2004) considered a three--step robust estimate for the regression parameter and the regression function. Besides, for the nonparametric regression setting,the sensitivity of the classical bandwidth selectors to anomalous data was discussed by several authors, such as, Leung, Marrot and Wu (1993), Wang and Scott (1994), Boente, Fraiman and Meloche (1997) and Cantoni and Roncheti (2001). In this talk, we will introduce a robust plug--in selector for the bandwidth, under a partly linear model  which converges to the optimal one and leads to robust data--driven estimates of the regression function and the regression parameter.the sensitivity of the classical bandwidth selectors to anomalous data was discussed by several authors, such as, Leung, Marrot and Wu (1993), Wang and Scott (1994), Boente, Fraiman and Meloche (1997) and Cantoni and Roncheti (2001). In this talk, we will introduce a robust plug--in selector for the bandwidth, under a partly linear model  which converges to the optimal one and leads to robust data--driven estimates of the regression function and the regression parameter. which converges to the optimal one and leads to robust data--driven estimates of the regression function and the regression parameter.