We consider the problem of estimating a probability density function based on data that are corrupted by noise from a uniform distribution. The (nonparametric) maximum likelihood estimator for the corresponding distribution function is well defined. For the density function this is not the case. We study two nonparametric estimators for this density. The first is a type of kernel density estimate based on the empirical distribution function of the observable data. The second is a kernel density estimate based on the MLE of the distribution function of the unobservable (uncorrupted) data. © VVS, 2003
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International audienceA density deconvolution problem with unknown distribution of the errors is con...
We show that maximum likelihood weighted kernel density estimation offers a unified approach to dens...
International audienceA density deconvolution problem with unknown distribution of the errors is con...
International audienceA density deconvolution problem with unknown distribution of the errors is con...
International audienceA density deconvolution problem with unknown distribution of the errors is con...
This book gives an introduction to deconvolution problems in nonparametric statistics, e.g. density ...
This thesis is concerned with the development of estimation techniques in four models involving stat...
We construct a density estimator and an estimator of the distribution function in the uniform deconv...
Abstract. In this tutorial paper we give an overview of deconvolution problems in nonparametric stat...
summary:We study the density deconvolution problem when the random variables of interest are an asso...
AbstractWe consider the problem of estimating a continuous bounded probability density function when...
We show that maximum likelihood weighted kernel density estimation offers a unified approach to dens...
The deconvolution kernel density estimator is a popular technique for solving the deconvolution prob...
International audienceA density deconvolution problem with unknown distribution of the errors is con...
This paper considers the problem of nonparametric deconvolution density estimation when sample obser...
International audienceA density deconvolution problem with unknown distribution of the errors is con...
We show that maximum likelihood weighted kernel density estimation offers a unified approach to dens...
International audienceA density deconvolution problem with unknown distribution of the errors is con...
International audienceA density deconvolution problem with unknown distribution of the errors is con...
International audienceA density deconvolution problem with unknown distribution of the errors is con...
This book gives an introduction to deconvolution problems in nonparametric statistics, e.g. density ...
This thesis is concerned with the development of estimation techniques in four models involving stat...