Add to ORKGWe study some problems of the parameter inference which are in connection with wide sense stationary long memory processes. Here we present the asymptotic behaviour of the corelation matrix and the limit distributions of the LSE for the regression coefficients in some types of linear models with singular Gaussian and non-Gaussian errors
We build the Conditional Least Squares Estimator of 0 based on the observation of a single trajecto...
International audienceIn this paper, we consider the usual linear regression model in the case where...
AbstractThe paper uses empirical process techniques to study the asymptotics of the least-squares es...
We study some problems of the parameter inference which are in connection with wide sense stationary...
Employing recent results of Robinson (2005) we consider the asymptotic properties of conditional-sum...
AbstractThis paper obtains a uniform reduction principle for the empirical process of a stationary m...
AbstractThis paper establishes the consistency and the root-n asymptotic normality of the exact maxi...
The associated R package 'slm' is available on the CRAN website (https://cran.r-project.org) or on t...
For the class of stationary Gaussian long memory processes, we study some properties of the least-sq...
Dans cette thèse, nous nous intéressons au modèle de régression linéaire usuel dans le cas où les er...
We present two approaches for linear prediction of long-memory time series. The first approach consi...
AbstractThe least squares (LS) estimator seems the natural estimator of the coefficients of a Gaussi...
International audienceThe behaviour of the LS estimator in a nonlinear regression model is investiga...
This paper obtains asymptotic representations of a class of L-estimators in a linear regression mode...
AbstractWe consider the problem of estimating regression models of two-dimensional random fields. As...
We build the Conditional Least Squares Estimator of 0 based on the observation of a single trajecto...
International audienceIn this paper, we consider the usual linear regression model in the case where...
AbstractThe paper uses empirical process techniques to study the asymptotics of the least-squares es...
We study some problems of the parameter inference which are in connection with wide sense stationary...
Employing recent results of Robinson (2005) we consider the asymptotic properties of conditional-sum...
AbstractThis paper obtains a uniform reduction principle for the empirical process of a stationary m...
AbstractThis paper establishes the consistency and the root-n asymptotic normality of the exact maxi...
The associated R package 'slm' is available on the CRAN website (https://cran.r-project.org) or on t...
For the class of stationary Gaussian long memory processes, we study some properties of the least-sq...
Dans cette thèse, nous nous intéressons au modèle de régression linéaire usuel dans le cas où les er...
We present two approaches for linear prediction of long-memory time series. The first approach consi...
AbstractThe least squares (LS) estimator seems the natural estimator of the coefficients of a Gaussi...
International audienceThe behaviour of the LS estimator in a nonlinear regression model is investiga...
This paper obtains asymptotic representations of a class of L-estimators in a linear regression mode...
AbstractWe consider the problem of estimating regression models of two-dimensional random fields. As...
We build the Conditional Least Squares Estimator of 0 based on the observation of a single trajecto...
International audienceIn this paper, we consider the usual linear regression model in the case where...
AbstractThe paper uses empirical process techniques to study the asymptotics of the least-squares es...