fully acknowledged. A key conceptual and methodological tool in time series modeling is the auto-covariance function, which, however, presupposes finite variances and excludes heavy tailed distributions and data. To allow for the latter, which are increas-ingly of interest in modern statistics, this paper introduces a “Gini autocovari-ance function ” defined under merely first order moment assumptions. Playing roles similar to those of the usual autocovariance function, it provides a new fundamental tool for nonparametric description and modeling of time series. It is seen how to fit autoregressive, moving average, and ARMA time series models using Gini autocovariances. Also, the Gini autocovariance function for a nonlinear heavy-tailed (Pa...
The paper treats the modeling of stationary multivariate stochastic processes via frequency domain,...
We use the sample covariations to estimate the parameters in a univariate symmetric stable autoregre...
In this paper a class of nonparametric transfer function models is proposed to model nonlinear relat...
fully acknowledged. A key conceptual and methodological tool in time series modeling is the auto-cov...
fully acknowledged. A key conceptual and methodological tool in time series modeling is the auto-cov...
When studying a real-life time series, it is frequently reasonable to assume, possibly after a suita...
This thesis provides a necessary and sufficient condition for asymptotic efficiency of a nonparametr...
A new class of time series models known as Generalized Autoregressive of order one with first-order ...
<p>The work revisits the autocovariance function estimation, a fundamental problem in statistical in...
The second order properties of a process are usually characterized by the autocovariance function. I...
This thesis is concerned with nonparametric techniques for inferring properties of time series. Firs...
International audienceThis paper proposes two novel alternative estimators for the autocovariance fu...
Abstract—We consider large scale covariance estimation using a small number of samples in applicatio...
The autoregressive model is a tool used in time series analysis to describe and model time series da...
The generalised autocovariance function is defined for a stationary stochastic process as the invers...
The paper treats the modeling of stationary multivariate stochastic processes via frequency domain,...
We use the sample covariations to estimate the parameters in a univariate symmetric stable autoregre...
In this paper a class of nonparametric transfer function models is proposed to model nonlinear relat...
fully acknowledged. A key conceptual and methodological tool in time series modeling is the auto-cov...
fully acknowledged. A key conceptual and methodological tool in time series modeling is the auto-cov...
When studying a real-life time series, it is frequently reasonable to assume, possibly after a suita...
This thesis provides a necessary and sufficient condition for asymptotic efficiency of a nonparametr...
A new class of time series models known as Generalized Autoregressive of order one with first-order ...
<p>The work revisits the autocovariance function estimation, a fundamental problem in statistical in...
The second order properties of a process are usually characterized by the autocovariance function. I...
This thesis is concerned with nonparametric techniques for inferring properties of time series. Firs...
International audienceThis paper proposes two novel alternative estimators for the autocovariance fu...
Abstract—We consider large scale covariance estimation using a small number of samples in applicatio...
The autoregressive model is a tool used in time series analysis to describe and model time series da...
The generalised autocovariance function is defined for a stationary stochastic process as the invers...
The paper treats the modeling of stationary multivariate stochastic processes via frequency domain,...
We use the sample covariations to estimate the parameters in a univariate symmetric stable autoregre...
In this paper a class of nonparametric transfer function models is proposed to model nonlinear relat...