Displaying similar documents to “Semiparametric deconvolution with unknown noise variance”

Adaptive estimation of the stationary density of discrete and continuous time mixing processes

Fabienne Comte, Florence Merlevède (2010)

ESAIM: Probability and Statistics

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In this paper, we study the problem of non parametric estimation of the stationary marginal density of an or a -mixing process, observed either in continuous time or in discrete time. We present an unified framework allowing to deal with many different cases. We consider a collection of finite dimensional linear regular spaces. We estimate using a projection estimator built on a data driven selected linear space among the collection. This data driven choice is performed the minimization...

Risk bounds for new M-estimation problems

Nabil Rachdi, Jean-Claude Fort, Thierry Klein (2013)

ESAIM: Probability and Statistics

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In this paper, we consider a new framework where two types of data are available: experimental data supposed to be i.i.d from and outputs from a simulated reduced model. We develop a procedure for parameter estimation to characterize a feature of the phenomenon . We prove a risk bound qualifying the proposed procedure in terms of the number of experimental data , reduced model complexity...

Adaptive non-asymptotic confidence balls in density estimation

Matthieu Lerasle (2012)

ESAIM: Probability and Statistics

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We build confidence balls for the common density of a real valued sample . We use resampling methods to estimate the projection of onto finite dimensional linear spaces and a model selection procedure to choose an optimal approximation space. The covering property is ensured for all  ≥ 2 and the balls are adaptive over a collection of linear spaces.

Adaptive non-asymptotic confidence balls in density estimation

Matthieu Lerasle (2012)

ESAIM: Probability and Statistics

Similarity:

We build confidence balls for the common density of a real valued sample . We use resampling methods to estimate the projection of onto finite dimensional linear spaces and a model selection procedure to choose an optimal approximation space. The covering property is ensured for all  ≥ 2 and the balls are adaptive over a collection of linear spaces.