Displaying similar documents to “Penalized nonparametric drift estimation for a continuously observed one-dimensional diffusion process”

Penalized nonparametric drift estimation for a continuously observed one-dimensional diffusion process

Eva Löcherbach, Dasha Loukianova, Oleg Loukianov (2011)

ESAIM: Probability and Statistics

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Let be a one dimensional positive recurrent diffusion continuously observed on [0,] . We consider a non parametric estimator of the drift function on a given interval. Our estimator, obtained using a penalized least square approach, belongs to a finite dimensional functional space, whose dimension is selected according to the data. The non-asymptotic risk-bound reaches the minimax optimal rate of convergence when → ∞. The main point of our work is that we do not suppose the process...

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

Emeline Schmisser (2013)

ESAIM: Probability and Statistics

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In this article, our aim is to estimate the successive derivatives of the stationary density of a strictly stationary and -mixing process (). This process is observed at discrete times  = 0 . The sampling interval can be fixed or small. We use a penalized least-square approach to compute adaptive estimators. If the derivative belongs to the Besov space B 2 , ∞ α B 2 , ∞ α , then our estimator converges at rate (). Then we consider a diffusion...

Linear diffusion with stationary switching regime

Xavier Guyon, Serge Iovleff, Jian-Feng Yao (2010)

ESAIM: Probability and Statistics

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Let be a Ornstein–Uhlenbeck diffusion governed by a stationary and ergodic process : ddd. We establish that under the condition with the stationary distribution of the regime process , the diffusion is ergodic. We also consider conditions for the existence of moments for the invariant law of when is a Markov jump process having a finite number of states. Using results on random difference equations on one hand and the fact that conditionally to , is Gaussian on the other...

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...