In this work, we introduce a smoothed influence function that constitute a theoretical tool for studying the outliers robustness properties of a large class of nonparametric estimators. With this tool, we first show the nonrobustness of the Nadaraya-Watson estimator of regression. Then we show...

This paper is the attempt to summarize the state of art in additive and generalized additive models (GAM). The emphasis is on approaches and numerical procedures which have emerged since the monograph of Hastie and Tibshirani (1990) although reconsidering certain aspects of their work. Apart...

A procedure for testing equality across nonparametric regressions is proposed. The procedure allows for any dimension of the explanatory variables and for any number of subsamples. We consider the case of random explanatory variables and allow the designs of the regressors and the number of...

We develop inference tools in a semiparametric regression model with missing response data. A semiparametric regression imputation estimator and an empirical likelihood based one for the mean of the response variable are defined. Both the estimators are proved to be asymptotically normal, with...

Additive modelling has been widely used in nonparametric regression to circumvent the "curse of dimensionality", by reducing the problem of estimating a multivariate regression function to the estimation of its univariate components. Estimation of these univariate functions, however, can suffer...

Additive modelling is known to be useful for multivariate nonparametric regression as it reduces the complexity of problem to the level of univariate regression. This usefulness could be compromised if the data set was contaminated by outliers whose detection and removal are particularly...

Chaudhuri, Doksum and Samarov (1997) have recently stressed the usefulness of the quantile regression formulation for survival analysis and for transformation models, more generally. In this paper, we explore the use of quantile regression in survival analysis by reanalysing a large experimental...

Applying nonparametric variable selection criteria in nonlinear regression models generally requires a substantial computational effort if the data set is large. In this paper we present a selection technique that is computationally much less demanding and performs well in comparison with...

We introduce a new method for the estimation of discount functions, yield curves and forward curves from government issued coupon bonds. Our approach is non-parametric and does not assume particular functional form for the discount function although we do show how to impose various restrictions...