Let (Xi)i=1 be an i.i.d. sample on having density f. Given a real function [phi] on with finite variation, and given an integer valued sequence (jn), let denote the estimator of f by wavelet projection based on [phi] and with multiresolution level equal to jn. We provide exact rates of almost...

Given an observation of the uniform empirical process [alpha]n, its functional increments [alpha]n(u+an[dot operator])-[alpha]n(u) can be viewed as a single random process, when u is distributed under the Lebesgue measure. We investigate the almost sure limit behaviour of the multivariate...

Times between consecutive events are often of interest in medical studies. Usually the events represent different states of the disease process and are modeled using multi-state models. This paper introduces and studies a feasible estimation method for the transition probabilities in a...

This paper considers the problem of parameter estimation in a general class of semiparametric models when observations are subject to missingness at random. The semiparametric models allow for estimating functions that are non-smooth with respect to the parameter. We propose a nonparametric...

This article proposes semi-parametric least squares estimation of parametric risk-return relationships, i.e. parametric restrictions between the conditional mean and the conditional variance of excess returns given a set of unobservable parametric factors. A distinctive feature of our estimator...

We show that difference-based methods can be used to construct simple and explicit estimators of error covariance and autoregressive parameters in nonparametric regression with time series errors. When the error process is Gaussian our estimators are efficient, but they are available well beyond...

In the common non-parametric regression model the problem of testing for the parametric form of the conditional variance is considered. A stochastic process based on the difference between the empirical processes that are obtained from the standardized non-parametric residuals under the null...

Let (<b>"X"</b>_<b>"i"</b>,<b>"Y"</b>_<b>"i"</b>) (<b>"i"</b>&equals;<b>"1"</b>,…,<b>"n"</b>) be "n" replications of a random vector (<b>"X"</b>,<b>"Y"</b> ), where "Y" is supposed to be subject to random right censoring. The data (<b>"X"</b>_<b>"i"</b>,<b>"Y"</b>_<b>"i"</b>) are assumed to come from a stationary "&agr;"-mixing process. We consider the problem of estimating the function...