Wir betrachten einen zeitlich inhomogenen Diffusionsprozess, der durch eine stochastische Differentialgleichung gegeben wird, deren Driftterm ein deterministisches T-periodisches Signal beinhaltet, dessen Periodizität bekannt ist. Dieses Signal sei in einem Besovraum enthalten. Wir schätzen es mit Hilfe eines nichtparametrischen Waveletschätzers. Unser Schätzer ist von einem Wavelet-Dichteschätzer mit Thresholding inspiriert, der 1996 in einem klassischen iid-Modell von Donoho, Johnstone, Kerkyacharian und Picard konstruiert wurde. Unter gewissen Ergodizitätsvoraussetzungen an den Prozess können wir nichtparametrische Konvergenzraten angegeben, die bis auf einen logarithmischen Term den Raten im klassischen iid-Fall entsprechen. Diese Raten...
We study the problem of estimating the spectral density of a stationary Gaussian time series. We use...
We consider nonparametric estimation of the parameter functions a(i)(.), i = 1, ..., p, of a time-va...
: Let (X t ) be a stictly stationary stochastic process (in continuous or discrete time). We are to ...
We study nonparametric estimation of the diffusion coefficient from discrete data, when the observat...
We study nonparametric estimation of the diffusion coefficient from discrete data, when the observat...
AbstractWe study the nonparametric estimation of the coefficients of a 1-dimensional diffusion proce...
International audienceFrom a wavelet analysis, one derives a nonparametrical estimator for the spect...
We suggest a new approach to wavelet threshold estimation of spectral densities of stationary time s...
International audienceFrom a wavelet analysis, one derives a nonparametrical estimator for the spect...
. We consider nonparametric estimation of the parameter functions a i (\Delta) , i = 1; : : : ; p ,...
We study Bayes procedures for the problem of nonparametric drift estimation for one-dimensional, erg...
Es soll eine Dichtefunktion geschätzt werden unter der Modellannahme, dass diese in einer geeigneten...
In this thesis, we consider a drift estimation problem of a certain class of stochastic periodic pro...
In the present paper we consider nonlinear wavelet estimators of the spectral density f of a zero me...
We consider nonparametric estimation of the transition operator P of a Markov chain and its transiti...
We study the problem of estimating the spectral density of a stationary Gaussian time series. We use...
We consider nonparametric estimation of the parameter functions a(i)(.), i = 1, ..., p, of a time-va...
: Let (X t ) be a stictly stationary stochastic process (in continuous or discrete time). We are to ...
We study nonparametric estimation of the diffusion coefficient from discrete data, when the observat...
We study nonparametric estimation of the diffusion coefficient from discrete data, when the observat...
AbstractWe study the nonparametric estimation of the coefficients of a 1-dimensional diffusion proce...
International audienceFrom a wavelet analysis, one derives a nonparametrical estimator for the spect...
We suggest a new approach to wavelet threshold estimation of spectral densities of stationary time s...
International audienceFrom a wavelet analysis, one derives a nonparametrical estimator for the spect...
. We consider nonparametric estimation of the parameter functions a i (\Delta) , i = 1; : : : ; p ,...
We study Bayes procedures for the problem of nonparametric drift estimation for one-dimensional, erg...
Es soll eine Dichtefunktion geschätzt werden unter der Modellannahme, dass diese in einer geeigneten...
In this thesis, we consider a drift estimation problem of a certain class of stochastic periodic pro...
In the present paper we consider nonlinear wavelet estimators of the spectral density f of a zero me...
We consider nonparametric estimation of the transition operator P of a Markov chain and its transiti...
We study the problem of estimating the spectral density of a stationary Gaussian time series. We use...
We consider nonparametric estimation of the parameter functions a(i)(.), i = 1, ..., p, of a time-va...
: Let (X t ) be a stictly stationary stochastic process (in continuous or discrete time). We are to ...