Tests for shift detection in locally-stationary autoregressive time series are constructed which resist contamination by a substantial amount of outliers. Tests based on a comparison of local medians standardized by a highly robust estimate of the variability show reliable performance in a broad variety of situations if the thresholds are adjusted for possible autocorrelations
The detection of structural breaks is an important task in many online-monitoring applications. Dyna...
textabstractRegime-switching models, like the smooth transition autoregressive (STAR) model are typi...
Autoregressive time series models of order p have p+2 parameters, the mean, the variance of the whit...
We use a local approach and highly robust estimators for the detection of level shifts in autocorre...
Abrupt shifts in the level of a time series represent important information and should be preserved...
Robustified rank tests, applying a robust scale estimator, are investigated for reliable and fast s...
We present a robust test for change-points in time series which is based on the two-sample Hodges-L...
The asymptotic distributions of Augmented-Dickey-Fuller (ADF) unit root tests for autoregressive pro...
In this paper we propose tests for the null hypothesis that a time series process displays a constan...
grantor: University of TorontoThe problem of change detection is about quick detection of ...
In this paper, we consider testing for linearity against a well-known class of regime switching mode...
In this note we discuss the properties of Augmented-Dickey-Fuller [ADF] unit root tests for autoregr...
The asymptotic distributions of Augmented-Dickey-Fuller (ADF) unit root tests for autoregressive pro...
We propose and investigate robust control charts for the detection of sudden shifts in sequences of ...
In this paper we derive tests for parameter constancy when the data generating process is non-statio...
The detection of structural breaks is an important task in many online-monitoring applications. Dyna...
textabstractRegime-switching models, like the smooth transition autoregressive (STAR) model are typi...
Autoregressive time series models of order p have p+2 parameters, the mean, the variance of the whit...
We use a local approach and highly robust estimators for the detection of level shifts in autocorre...
Abrupt shifts in the level of a time series represent important information and should be preserved...
Robustified rank tests, applying a robust scale estimator, are investigated for reliable and fast s...
We present a robust test for change-points in time series which is based on the two-sample Hodges-L...
The asymptotic distributions of Augmented-Dickey-Fuller (ADF) unit root tests for autoregressive pro...
In this paper we propose tests for the null hypothesis that a time series process displays a constan...
grantor: University of TorontoThe problem of change detection is about quick detection of ...
In this paper, we consider testing for linearity against a well-known class of regime switching mode...
In this note we discuss the properties of Augmented-Dickey-Fuller [ADF] unit root tests for autoregr...
The asymptotic distributions of Augmented-Dickey-Fuller (ADF) unit root tests for autoregressive pro...
We propose and investigate robust control charts for the detection of sudden shifts in sequences of ...
In this paper we derive tests for parameter constancy when the data generating process is non-statio...
The detection of structural breaks is an important task in many online-monitoring applications. Dyna...
textabstractRegime-switching models, like the smooth transition autoregressive (STAR) model are typi...
Autoregressive time series models of order p have p+2 parameters, the mean, the variance of the whit...
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