In this paper, we consider a multidimensional convolution model for which we provide
adaptive anisotropic kernel estimators of a signal density f measured with additive error. For …

In this work, we develop a new smooth backfitting method and theory for estimating additive
nonparametric regression models when the covariates are contaminated by measurement …

This paper develops a method to construct uniform confidence bands in deconvolution when
the error distribution is unknown. Simulation studies demonstrate the performance of the …

Quantile estimation in deconvolution problems is studied comprehensively. In particular, the
more realistic setup of unknown error distributions is covered. Our plug-in method is based …

This paper considers treatment effects under endogeneity with complex heterogeneity in the
selection equation. We model the outcome of an endogenous treatment as a triangular …

We investigate the data driven choice of the cutoff parameter in density deconvolution
problems with unknown error distribution. To make the target density identifiable, one has to …

This paper surveys new estimators of the density of a random effect in linear mixed-effects
models. Data are contaminated by random noise, and we do not observe directly the …

Given a sample from a discretely observed Lévy process X=(Xt) t≥ 0 of the finite jump
activity, the problem of nonparametric estimation of the Lévy density ρ corresponding to the …

We consider a circular deconvolution problem, in which the density f of a circular random
variable X must be estimated nonparametrically based on an iid sample from a noisy …

In this paper, we consider the problem of adaptive density or survival function estimation in
an additive model defined by Z= X+ Y with X independent of Y, when both random variables …