This article studies estimation of a stationary autocovariance structure in the presence of an unknown number of mean shifts. Here, a Yule-Walker moment estimator for the autoregressive parameters in a dependent time series contaminated by mean shift changepoints is proposed and studied. The estimator is based on first order differences of the series and is proven consistent and asymptotically normal when the number of changepoints $m$ and the series length $N$ satisfy $m/N \rightarrow 0$ as $N \rightarrow \infty$
In this thesis tests for a change in multivariate autoregressive time series are introduced and furt...
This paper contains a nonlinear, nonstationary autoregressive model whose intercept changes determin...
A common challenge in time series is to forecast data that suffer from structural breaks or changepo...
International audienceWe consider the problem of multiple change-point estimation in the mean of an ...
International audienceWe consider the problem of multiple change-point estimation in the mean of an ...
International audienceWe consider the problem of multiple change-point estimation in the mean of an ...
In numerous industrial applications, organizations wish to monitor time series data to better unders...
Abstract. We consider the problem of multiple change-point estimation in the mean of a Gaussian AR(1...
Control charts are used to detect changes in a process. Once a change is detected, knowledge of the ...
In numerous industrial applications, organizations wish to monitor time series data to better unders...
This paper analyzes vector autoregressive models (VAR) with multiple struc-tural changes. One distin...
Abstract: It is quite common that the structure of a time series changes abruptly. Identifying these...
We propose a novel and unified framework for change-point estimation in multivariate time series. Th...
The most commonly used method for estimating the time domain parameters of an autoregressive process...
The most commonly used method for estimating the time domain parameters of an autoregressive process...
In this thesis tests for a change in multivariate autoregressive time series are introduced and furt...
This paper contains a nonlinear, nonstationary autoregressive model whose intercept changes determin...
A common challenge in time series is to forecast data that suffer from structural breaks or changepo...
International audienceWe consider the problem of multiple change-point estimation in the mean of an ...
International audienceWe consider the problem of multiple change-point estimation in the mean of an ...
International audienceWe consider the problem of multiple change-point estimation in the mean of an ...
In numerous industrial applications, organizations wish to monitor time series data to better unders...
Abstract. We consider the problem of multiple change-point estimation in the mean of a Gaussian AR(1...
Control charts are used to detect changes in a process. Once a change is detected, knowledge of the ...
In numerous industrial applications, organizations wish to monitor time series data to better unders...
This paper analyzes vector autoregressive models (VAR) with multiple struc-tural changes. One distin...
Abstract: It is quite common that the structure of a time series changes abruptly. Identifying these...
We propose a novel and unified framework for change-point estimation in multivariate time series. Th...
The most commonly used method for estimating the time domain parameters of an autoregressive process...
The most commonly used method for estimating the time domain parameters of an autoregressive process...
In this thesis tests for a change in multivariate autoregressive time series are introduced and furt...
This paper contains a nonlinear, nonstationary autoregressive model whose intercept changes determin...
A common challenge in time series is to forecast data that suffer from structural breaks or changepo...