Random coefficient regression models have been applied in different fields and they constitute a unifying setup for many statistical problems. The nonparametric study of this model started with Beran and Hall (1992) and it has become a fruitful framework. In this paper we propose and study...

In the fixed design regression model, additional weights are considered for the Nadaraya--Watson and Gasser--M\"uller kernel estimators. We study their asymptotic behavior and the relationships between new and classical estimators. For a simple family of weights, and considering the IMSE as...

The objective of this paper is to compare the performance of two predictive radiological models, logistic regression (LR) and neural network (NN), with five different resampling methods. One hundred and sixty-seven patients with proven calvarial lesions as the only known disease were enrolled....

This paper presents a comparative analysis of linear and mixed models for short term forecasting of a real data series with a high percentage of missing data. Data are the series of significant wave heights registered at regular periods of three hours by a buoy placed in the Bay of Biscay. The...

We introduce simple nonparametric density estimators that generalize the classical histogram and frequency polygon. The new estimators are expressed as linear combination of density functions that are piecewise polynomials, where the coefficients are optimally chosen in order to minimize the...

Principal curves have been defined Hastie and Stuetzle (JASA, 1989) as smooth curves passing through the middle of a multidimensional data set. They are nonlinear generalizations of the first principal component, a characterization of which is the basis for the principal curves definition. In...