Add to ORKGWe constuct a sequential adaptive procedure for estimating the autoregressive function at a given point in nonparametric autoregression models with Gaussian noise. We make use of the sequential kernel estimators. The optimal adaptive convergence rate is given as well as the upper bound for the minimax risk
This article revisits a sequential approach to the estimation of the parameter in a p-order autoregr...
Efficient 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...
International audienceThis paper deals with the estimation of a autoregression function at a given p...
International audienceWe construct a robust truncated sequential estimator for the point- wise estim...
We construct efficient robust truncated sequential estimators for the pointwise estimation problem i...
In this paper for the first time the nonparametric autoregression estimation problem for the quadrat...
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...
In this paper for the first time the adaptive efficient estimation problem for nonparametric autoreg...
This paper considers adaptive estimation in nonstationary autoregressive moving average models with ...
For an autoregressive process of order p, the paper proposes new sequential estimates for the unknow...
In a time series regression model the residual autoregression function is an unknown, possibly non-l...
In this paper we consider high dimension models based on dependent observations defined through auto...
This article revisits a sequential approach to the estimation of the parameter in a p-order autoregr...
Efficient 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...
International audienceThis paper deals with the estimation of a autoregression function at a given p...
International audienceWe construct a robust truncated sequential estimator for the point- wise estim...
We construct efficient robust truncated sequential estimators for the pointwise estimation problem i...
In this paper for the first time the nonparametric autoregression estimation problem for the quadrat...
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...
In this paper for the first time the adaptive efficient estimation problem for nonparametric autoreg...
This paper considers adaptive estimation in nonstationary autoregressive moving average models with ...
For an autoregressive process of order p, the paper proposes new sequential estimates for the unknow...
In a time series regression model the residual autoregression function is an unknown, possibly non-l...
In this paper we consider high dimension models based on dependent observations defined through auto...
This article revisits a sequential approach to the estimation of the parameter in a p-order autoregr...
Efficient 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...