Nonparametric estimation of the derivatives of the stationary density for stationary processes

Emeline Schmisser

Displaying similar documents to “Nonparametric estimation of the derivatives of the stationary density for stationary processes”

Discrete sampling of an integrated diffusion process and parameter estimation of the diffusion coefficient

Arnaud Gloter (2010)

ESAIM: Probability and Statistics

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Let () be a diffusion on the interval and a sequence of positive numbers tending to zero. We define as the integral between and of . We give an approximation of the law of by means of a Euler scheme expansion for the process (). In some special cases, an approximation by an explicit Gaussian ARMA(1,1) process is obtained. When we deduce from this expansion estimators of the diffusion coefficient of based on (). These estimators are shown to...

Estimation in autoregressive model with measurement error

Jérôme Dedecker, Adeline Samson, Marie-Luce Taupin (2014)

ESAIM: Probability and Statistics

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Consider an autoregressive model with measurement error: we observe = + , where the unobserved is a stationary solution of the autoregressive equation = ( ) + . The regression function is known up to a finite dimensional parameter to be estimated. The distributions of and are unknown and...

Hydrodynamic limit of a d-dimensional exclusion process with conductances

Fábio Júlio Valentim (2012)

Annales de l'I.H.P. Probabilités et statistiques

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Fix a polynomial of the form () = + ∑2≤≤ =1 with (1) gt; 0. We prove that the evolution, on the diffusive scale, of the empirical density of exclusion processes on $𝕋^{d}$ , with conductances given by special class of functions, is described by the unique weak solution of the non-linear parabolic partial differential equation = ∑ ...

Model selection and estimation of a component in additive regression

Xavier Gendre (2014)

ESAIM: Probability and Statistics

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Let ∈ ℝ be a random vector with mean and covariance matrix where is some known × -matrix. We construct a statistical procedure to estimate as well as under moment condition on or Gaussian hypothesis. Both cases are developed for known or unknown . Our approach is free from any prior assumption on and is based on non-asymptotic model selection methods....