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In this paper we study the problem of density deconvolution under general assumptions on the measurement error distribution. Typically deconvolution estimators are constructed using Fourier transform techniques, and it is assumed that the…

Statistics Theory · Mathematics 2020-02-04 Denis Belomestny , Alexander Goldenshluger

We consider the problem of estimating a density $f_X$ using a sample $Y_1,...,Y_n$ from $f_Y=f_X\star f_{\epsilon}$, where $f_{\epsilon}$ is an unknown density. We assume that an additional sample $\epsilon_1,...,\epsilon_m$ from…

Statistics Theory · Mathematics 2009-08-21 Jan Johannes

This paper considers the problem of adaptive estimation of a mean pattern in a randomly shifted curve model. We show that this problem can be transformed into a linear inverse problem, where the density of the random shifts plays the role…

Statistics Theory · Mathematics 2010-10-21 Jérémie Bigot , Sébastien Gadat

We consider a circular deconvolution problem, in which the density $f$ of a circular random variable $X$ must be estimated nonparametrically based on an i.i.d. sample from a noisy observation $Y$ of $X$. The additive measurement error is…

Statistics Theory · Mathematics 2013-12-11 Jan Johannes , Maik Schwarz

In the present paper we consider the problem of estimating a periodic $(r+1)$-dimensional function $f$ based on observations from its noisy convolution. We construct a wavelet estimator of $f$, derive minimax lower bounds for the $L^2$-risk…

Statistics Theory · Mathematics 2013-05-24 Rida Benhaddou , Marianna Pensky , Dominique Picard

Density deconvolution deals with the estimation of the probability density function $f$ of a random signal from $n\geq1$ data observed with independent and known additive random noise. This is a classical problem in statistics, for which…

Methodology · Statistics 2024-12-16 Stefano Favaro , Sandra Fortini

Given an i.i.d. sample from a distribution $F$ on $\mathbb{R}$ with uniformly continuous density $p_0$, purely data-driven estimators are constructed that efficiently estimate $F$ in sup-norm loss and simultaneously estimate $p_0$ at the…

Statistics Theory · Mathematics 2011-01-10 Evarist Giné , Richard Nickl

We consider the problem of estimating the unknown response function in the multichannel deconvolution model with long-range dependent Gaussian errors. We do not limit our consideration to a specific type of long-range dependence rather we…

Statistics Theory · Mathematics 2016-09-29 Rida Benhaddou , Rafal Kulik , Marianna Pensky , Theofanis Sapatinas

Bayesian density deconvolution using nonparametric prior distributions is a useful alternative to the frequentist kernel based deconvolution estimators due to its potentially wide range of applicability, straightforward uncertainty…

Statistics Theory · Mathematics 2013-09-10 Abhra Sarkar , Debdeep Pati , Bani K. Mallick , Raymond J. Carroll

We introduce a new procedure to select the optimal cutoff parameter for Fourier density estimators that leads to adaptive rate optimal estimators, up to a logarithmic factor. This adaptive procedure applies for different inverse problems.…

Statistics Theory · Mathematics 2018-02-15 Céline Duval , Johanna Kappus

We consider the model $Z_i=X_i+\varepsilon_i$, for i.i.d. $X_i$'s and $\varepsilon_i$'s and independent sequences $(X_i)_{i\in{\mathbb{N}}}$ and $(\varepsilon_i)_{i\in{\mathbb{N}}}$. The density $f_{\varepsilon}$ of $\varepsilon_1$ is…

Statistics Theory · Mathematics 2009-02-10 C. Butucea , F. Comte

We consider a multiplicative deconvolution problem, in which the density $f$ or the survival function $S^X$ of a strictly positive random variable $X$ is estimated nonparametrically based on an i.i.d. sample from a noisy observation $Y =…

Statistics Theory · Mathematics 2025-09-30 Sergio Brenner Miguel , Jan Johannes , Maximilian Siebel

We consider the problem of estimating the density $g$ of identically distributed variables $X\_i$, from a sample $Z\_1, ..., Z\_n$ where $Z\_i=X\_i+\sigma\epsilon\_i$, $i=1, ..., n$ and $\sigma \epsilon\_i$ is a noise independent of $X\_i$…

Statistics Theory · Mathematics 2008-02-11 Fabienne Comte , Yves Rozenholc , Marie-Luce Taupin

The present paper considers a problem of estimating a linear functional $\Phi=\int_{-\infty}^\infty \varphi(x) f(x)dx$ of an unknown deconvolution density $f$ on the basis of i.i.d. observations $Y_i = \theta_i + \xi_i$ where $\xi_i$ has a…

Statistics Theory · Mathematics 2015-05-19 Marianna Pensky

Let $X_1,...,X_n$ be i.i.d. observations, where $X_i=Y_i+\sigma_n Z_i$ and the $Y$'s and $Z$'s are independent. Assume that the $Y$'s are unobservable and that they have the density $f$ and also that the $Z$'s have a known density $k.$…

Statistics Theory · Mathematics 2018-04-17 Shota Gugushvili , Bert van Es

We consider multichannel deconvolution in a periodic setting with long-memory errors under three different scenarios for the convolution operators, i.e., super-smooth, regular-smooth and box-car convolutions. We investigate global…

Statistics Theory · Mathematics 2014-05-07 Rafal Kulik , Theofanis Sapatinas , Justin Rory Wishart

Let $p_n(y)=\sum_k\hat{\alpha}_k\phi(y-k)+\sum_{l=0}^{j_n-1}\sum_k\hat {\beta}_{lk}2^{l/2}\psi(2^ly-k)$ be the linear wavelet density estimator, where $\phi$, $\psi$ are a father and a mother wavelet (with compact support),…

Statistics Theory · Mathematics 2009-08-31 Evarist Giné , Richard Nickl

Let $X_1,..., X_n$ be i.i.d.\ copies of a random variable $X=Y+Z,$ where $ X_i=Y_i+Z_i,$ and $Y_i$ and $Z_i$ are independent and have the same distribution as $Y$ and $Z,$ respectively. Assume that the random variables $Y_i$'s are…

Statistics Theory · Mathematics 2018-04-17 Shota Gugushvili , Bert van Es , Peter Spreij

Motivated by problems in control theory concerning decay rates for the damped wave equation $$w_{tt}(x,t) + \gamma(x) w_t(x,t) + (-\Delta + 1)^{s/2} w(x,t) = 0,$$ we consider an analogue of the classical Paneah-Logvinenko-Sereda theorem for…

Classical Analysis and ODEs · Mathematics 2026-04-30 Benjamin Jaye , Rahul Sethi

We extend deconvolution in a periodic setting to deal with functional data. The resulting functional deconvolution model can be viewed as a generalization of a multitude of inverse problems in mathematical physics where one needs to recover…

Statistics Theory · Mathematics 2009-03-09 Marianna Pensky , Theofanis Sapatinas
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