Graciela Boente

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Detecting outlying observations is an important step in any analysis, even when robust estimates are used. In particular, the robustified Mahalanobis distance is a natural measure of outlyingness if one focuses on ellipsoidal distributions. However, it is well known that the asymptotic chi-square approximation for the cutoff value of the Mahalanobis(More)
In this paper, we introduce a family of robust estimates for the parametric and nonparametric components under a generalized partially linear model, where the data are modeled by yi|(xi, ti)∼ F (·, μi) with μi = H(η(ti) + x T i β), for some known distribution function F and link function H . It is shown that the estimates of β are root-n consistent and(More)
In this paper, under a semiparametric partly linear regression model with fixed design, we introduce a family of robust procedures to select the bandwidth parameter. The robust plug–in proposal is based on nonparametric robust estimates of the ν−th derivatives and under mild conditions, it converges to the optimal bandwidth. A robust cross–validation(More)
In this paper, under a nonparametric regression model, we introduce a family of robust procedures to estimate the regression funtion when missing data occur in the response. Our proposal is based on a local M−functional applied to the conditional distribution function estimate adapted to the presence of missing data. We show that the robust procedure is(More)
Abreviated Title: Qualitative Robustness SUMMARY In this paper we generalize Hampel's definition of robustness and IT-robustness of a sequence of estimators to the case of non i.i.d. stochastic processes, using appropriate metrics on the space of finite and infinite dimensional samples. We also present a different approach to qualitative robust-ness based(More)
When dealing with situations in which the responses are discrete or show some type of asymmetry, the linear model is not appropriate to establish the relation between the responses and the covariates. Generalized linear models serve this purpose, since they allow one to model the mean of the responses through a link function, linearly on the covariates.(More)
In many situations, when dealing with several populations with different covariance operators, equality of the operators is assumed. Usually, if this assumption does not hold, one estimates the covariance operator of each group separately, which leads to a large number of parameters. As in the multivariate setting, this is not satisfactory since the(More)
Under normality, Flury and Schmid [15] investigated the asymptotic properties of the quadratic discrimination procedure under hierarchical models for the scatter matrices, that is, (i) arbitrary scatter matrices, (ii) common principal components, (iii) proportional scatter matrices and (iv) identical matrices. In this paper, we study the properties of(More)