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Nonparametric deconvolution problem for dependent sequences
long range dependence linear processes error-in-variables models deconvolution
2009/9/16
We consider the nonparametric estimation of the density function of weakly and strongly dependent processes with noisy observations. We show that in the ordinary smooth case the optimal bandwidth choi...
Deconvolution for an atomic distribution
Asymptotic normality atomic distribution deconvolution kernel density estimator
2009/9/16
Let $X_1,ldots, X_n$ be i.i.d. observations, where $X_i=Y_i+sigma Z_i $ and $Y_i$ and $Z_i$ are independent. Assume that unobservable $Y$'s are distributed as a random variable $UV$, where $U$ and $V$...
Deconvolution with unknown error distribution
Deconvolution Fourier transform kernel estimation spectralcut off Sobolev space source condition optimal rate of convergence
2010/4/29
We assume that an additional sample x1, . . . , xm from fx is observed.
Estimators of fX and its derivatives are constructed by using nonparametric
estimators of fY and fx and by applying a spectral...
On the usefulness of Meyer wavelets for deconvolution and density estimation
Density estimation Deconvolution Inverse problem Wavelet thresholding Random thresholds Oracle inequalities
2010/3/18
The aim of this paper is to show the usefulness of Meyer wavelets for the classical problem of
density estimation and for density deconvolution fromnoisy observations. By using suchwavelets, the comp...
Functional deconvolution in a periodic setting：Uniform case
Adaptivity Besov spaces block thresholding deconvolution Fourier analysis functional data Meyer wavelets
2010/4/27
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 mathe...
Deconvolution density estimation with heteroscedastic errors using SIMEX
Density estimation deconvolution measurementerrors SIMEX heteroscedasticity
2010/3/18
In many real applications, the distribution of measurement error
could vary with each subject or even with each observation so the errors
are heteroscedastic. In this paper, we propose a fast algori...
Testing distribution in deconvolution problems
contaminated data Laguerre polynomials Meixnerpolynomials Legendre polynomials
2010/3/17
In this paper we consider a random variable Y contamined by
an independent additive noise Z.We assume that Z has known distribution.
Our purpose is to test the distribution of the unobserved random ...
Unsupervised bayesian convex deconvolution based on a field with an explicit partition function
Deconvolution Bayesian statistics regularization convex potentials partition function hyperparametersestimation
2010/3/17
This paper proposes a non-Gaussian Markov field with a special feature: an explicit partition function.To the best of our knowledge, this is an original contribution. Moreover, the explicit expression...
Data-driven efficient score tests for deconvolution problems
Hypothesis testing statistical inverse problems deconvolution efficient score test model selection data-driven test
2010/4/30
We consider testing statistical hypotheses about densities of
signals in deconvolution models. A new approach to this problem is proposed.
We constructed score tests for the deconvolution with the k...
Undercomplete Blind Subspace Deconvolution via Linear Prediction
Undercomplete Blind Subspace Deconvolution Linear Prediction
2010/4/29
Undercomplete Blind Subspace Deconvolution via Linear Prediction。
Undercomplete Blind Subspace Deconvolution
Undercomplete Blind Subspace Deconvolution blind source deconvolution
2010/4/26
We introduce the blind subspace deconvolution (BSSD) problem, which is the extension of both the blind source deconvolution (BSD) and the independent subspace analysis (ISA) tasks. We examine the case...