Add to ORKGThe class of locally stationary wavelet processes is a wavelet-based model for covariance nonstationary zero mean time series. This paper presents an algorithm for the pointwise adaptive estimation of their time-varying spectral density. The performance of the pro- cedure is evaluated on simulated and real time series. Two applications of the procedure are also presented and evaluated on real data. The first is a test of local significance for the coefficients of the so-called wavelet periodogram. The second is a new test of covariance stationarity
This thesis proposes novel methods for the modelling of multivariate time series. The work presented...
Time series data can often possess complex and dynamic characteristics. Two key statistical properti...
In the present paper we consider nonlinear wavelet estimators of the spectral density f of a zero me...
The class of locally stationary wavelet processes is a wavelet-based model for covariance nonstation...
We introduce a wavelet-based model of local stationarity. This model enlarges the class of locally s...
This article defines and studies a new class of non-stationary random processes constructed from dis...
This article defines and studies a new class of non-stationary random processes constructed from dis...
We fit a class of semiparametric models to a nonstationary process. This class is parametrized by a ...
This article gives an overview on nonparametric modelling of nonstationary time series and estimatio...
In time series analysis, most of the models are based on the assumption of covariance stationarity. ...
AbstractWe fit a class of semiparametric models to a nonstationary process. This class is parametriz...
We introduce and examine particular wavelet-based decompositions of stationary time series in discre...
In this paper, we study the problem of adaptive estimation of the spectral density of a stationary G...
Time series data can often possess complex and dynamic characteristics. Two key statistical properti...
This thesis proposes novel methods for the modelling of multivariate time series. The work presented...
This thesis proposes novel methods for the modelling of multivariate time series. The work presented...
Time series data can often possess complex and dynamic characteristics. Two key statistical properti...
In the present paper we consider nonlinear wavelet estimators of the spectral density f of a zero me...
The class of locally stationary wavelet processes is a wavelet-based model for covariance nonstation...
We introduce a wavelet-based model of local stationarity. This model enlarges the class of locally s...
This article defines and studies a new class of non-stationary random processes constructed from dis...
This article defines and studies a new class of non-stationary random processes constructed from dis...
We fit a class of semiparametric models to a nonstationary process. This class is parametrized by a ...
This article gives an overview on nonparametric modelling of nonstationary time series and estimatio...
In time series analysis, most of the models are based on the assumption of covariance stationarity. ...
AbstractWe fit a class of semiparametric models to a nonstationary process. This class is parametriz...
We introduce and examine particular wavelet-based decompositions of stationary time series in discre...
In this paper, we study the problem of adaptive estimation of the spectral density of a stationary G...
Time series data can often possess complex and dynamic characteristics. Two key statistical properti...
This thesis proposes novel methods for the modelling of multivariate time series. The work presented...
This thesis proposes novel methods for the modelling of multivariate time series. The work presented...
Time series data can often possess complex and dynamic characteristics. Two key statistical properti...
In the present paper we consider nonlinear wavelet estimators of the spectral density f of a zero me...