Abstract
In this paper we consider the convolutionmodel Z = X + Y withX of unknown density f, independent of Y, when both random variables are nonnegative. Our goal is to estimate the unknown density f of X from n independent identically distributed observations of Z, when the law of the additive process Y is unknown. When the density of Y is known, a solution to the problem has been proposed in [17]. To make the problem identifiable for unknown density of Y, we assume that we have access to a preliminary sample of the nuisance process Y. The question is to propose a solution to an inverse problem with unknown operator. To that aim, we build a family of projection estimators of f on the Laguerre basis, well-suited for nonnegative random variables. The dimension of the projection space is chosen thanks to a model selection procedure by penalization. At last we prove that the final estimator satisfies an oracle inequality. It can be noted that the study of the mean integrated square risk is based on Bernstein’s type concentration inequalities developed for random matrices in [23].
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Comte, F., Mabon, G. Laguerre deconvolution with unknown matrix operator. Math. Meth. Stat. 26, 237–266 (2017). https://doi.org/10.3103/S1066530717040019
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DOI: https://doi.org/10.3103/S1066530717040019
Keywords
- convolution model
- linear inverse problem
- nonnegative random variables
- Laguerre basis
- nonparametric density estimation
- random matrix
- oracle inequalities
- adaptive estimation