We study the problem of estimating some unknown regression or autoregression function in a B-mixing dependent framework (for the design or the errors). For this end, we consider some collection of models which are finite dimensional spaces. A penalized least-squares estimator (PLSE) is built on...

This paper proposes to use a semi parametric regression method, named Sliced Inverse Regression (SIR hereafter), to analyse ambulatory blood pressure monitoring data.

This paper presents a new simulated maximum-likelihood method that rests on estimating the likelihood nonparametrically on a simulated sample. We prove that this method, which can be used on very general models, is consistent and asymptotically efficient.

We propose a minimax statistical framework adapted to nonparametric estimation in deterministic dynamical system, which highlights the importance of the recurrence property of the observed process. Estimators are evaluated on the basis of a weighted uniform norm. The weight function rn depends...

The least-absolute-deviations (LAD) estimator for a median- regression model does not satisfy the standard conditions for obtaining asymptotic refinements through use of the bootstrap because the LAD objective function is not smooth. This paper overcomes this problem by smoothing the objective...

In this paper, I assess the employment and income effect of divorce for women in West Germany between 2000 and 2005. With newly available administrative data that allows me to adopt a causal approach, I find strong negative employment effects with respect to marginal employment and strong...