Add to ORKGInternational audienceThis paper deals with the estimation of a autoregression function at a given point in nonparametric autoregression models with Gaussian noise. An adaptive kernel estimator which attains the minimax rate is constructed for the minimax risk
In a time series regression model the residual autoregression function is an unknown, possibly non-l...
E ¢ cient semiparametric and parametric estimates are developed for a spatial autoregressive model, ...
Godambe's (1985) theorem on optimal estimating equations for stochastic processes is applied to nonp...
We constuct a sequential adaptive procedure for estimating the autoregressive function at a given po...
This thesis is devoted to nonparametric estimation for autoregressive models. We consider the proble...
14 pagesThe paper deals with the nonparametric estimation problem at a given fixed point for an auto...
The paper deals with estimating problem of regression function at a given state point in nonparametr...
We develop asymptotic theory for nonparametric estimators of the autoregression function. To deal wi...
In this paper for the first time the adaptive efficient estimation problem for nonparametric autoreg...
We construct efficient robust truncated sequential estimators for the pointwise estimation problem i...
International audienceWe construct a robust truncated sequential estimator for the point- wise estim...
This paper considers adaptive estimation in nonstationary autoregressive moving average models with ...
Suppose we observe an ergodic Markov chain on the real line, with a parametric model for the autoreg...
Cette thèse se consacre à l'estimation non paramétrique pour les modèles autorégressifs. Nous consid...
Efficient semiparametric and parametric estimates are developed for a spatial autoregressive model, ...
In a time series regression model the residual autoregression function is an unknown, possibly non-l...
E ¢ cient semiparametric and parametric estimates are developed for a spatial autoregressive model, ...
Godambe's (1985) theorem on optimal estimating equations for stochastic processes is applied to nonp...
We constuct a sequential adaptive procedure for estimating the autoregressive function at a given po...
This thesis is devoted to nonparametric estimation for autoregressive models. We consider the proble...
14 pagesThe paper deals with the nonparametric estimation problem at a given fixed point for an auto...
The paper deals with estimating problem of regression function at a given state point in nonparametr...
We develop asymptotic theory for nonparametric estimators of the autoregression function. To deal wi...
In this paper for the first time the adaptive efficient estimation problem for nonparametric autoreg...
We construct efficient robust truncated sequential estimators for the pointwise estimation problem i...
International audienceWe construct a robust truncated sequential estimator for the point- wise estim...
This paper considers adaptive estimation in nonstationary autoregressive moving average models with ...
Suppose we observe an ergodic Markov chain on the real line, with a parametric model for the autoreg...
Cette thèse se consacre à l'estimation non paramétrique pour les modèles autorégressifs. Nous consid...
Efficient semiparametric and parametric estimates are developed for a spatial autoregressive model, ...
In a time series regression model the residual autoregression function is an unknown, possibly non-l...
E ¢ cient semiparametric and parametric estimates are developed for a spatial autoregressive model, ...
Godambe's (1985) theorem on optimal estimating equations for stochastic processes is applied to nonp...