Density estimation in the uniform deconvolution model

P. Groeneboom, G. Jongbloed

Research output: Contribution to JournalArticleAcademicpeer-review


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.
Original languageEnglish
Pages (from-to)136-157
JournalStatistica Neerlandica. Journal of the Netherlands Society for Statistics and Operations Research
Issue number1
Publication statusPublished - 2003

Bibliographical note

MR2035863 Incomplete data: multiple imputation and model-based analysis (Utrecht, 2001)


Dive into the research topics of 'Density estimation in the uniform deconvolution model'. Together they form a unique fingerprint.

Cite this