We develop a test for stationarity of a time series against the alternative of a time-changing covariance structure. Using localized versions of the periodogram, we obtain empirical versions of a reasonable notion of a time-varying spectral density. Coefficients w.r.t. a Haar wavelet series expansion of such a time-varying periodogram are a possible indicator whether there is some deviation from covariance stationarity. We propose a test based on the limit distribution of these empirical coefficients
International audienceWavelet analysis is now frequently used to extract information from ecological...
International audienceVarious forms of instability can be observed in macroeconomic and financial da...
The study of the correlations that may exist between neurophysiological signals is at the heart of m...
In the present paper, we propose a wavelet-based hypothesis test for second-order stationarity in a ...
The first paper describes an alternative approach for testing the existence of trend among time seri...
Wavelets provide the flexibility to analyse stochastic processes at different scales. Here, we apply...
The class of locally stationary wavelet processes is a wavelet-based model for covariance nonstation...
This thesis deals with the applications of wavelet theory to time series data. We first focus on sta...
In one approach to spectral estimation, a sample record is broken into a number of disjoint sections...
Interest in functional time series has spiked in the recent past with papers covering both methodolo...
A frequency-domain statistic is introduced to test for stationarity versus stochastic or determinist...
In this paper, we present approximate distributions for the ratio of the cumulative wavelet periodog...
The principle of stationarity plays an important role in time series analysis. A key assumption in c...
My thesis investigates wavelet theory and methods underlying recent applications to time series anal...
Wavelets provide the flexibility to analyse stochastic processes at different scales. Here, we apply...
International audienceWavelet analysis is now frequently used to extract information from ecological...
International audienceVarious forms of instability can be observed in macroeconomic and financial da...
The study of the correlations that may exist between neurophysiological signals is at the heart of m...
In the present paper, we propose a wavelet-based hypothesis test for second-order stationarity in a ...
The first paper describes an alternative approach for testing the existence of trend among time seri...
Wavelets provide the flexibility to analyse stochastic processes at different scales. Here, we apply...
The class of locally stationary wavelet processes is a wavelet-based model for covariance nonstation...
This thesis deals with the applications of wavelet theory to time series data. We first focus on sta...
In one approach to spectral estimation, a sample record is broken into a number of disjoint sections...
Interest in functional time series has spiked in the recent past with papers covering both methodolo...
A frequency-domain statistic is introduced to test for stationarity versus stochastic or determinist...
In this paper, we present approximate distributions for the ratio of the cumulative wavelet periodog...
The principle of stationarity plays an important role in time series analysis. A key assumption in c...
My thesis investigates wavelet theory and methods underlying recent applications to time series anal...
Wavelets provide the flexibility to analyse stochastic processes at different scales. Here, we apply...
International audienceWavelet analysis is now frequently used to extract information from ecological...
International audienceVarious forms of instability can be observed in macroeconomic and financial da...
The study of the correlations that may exist between neurophysiological signals is at the heart of m...