Name
Abstract | ||
|---|---|---|
Testing the stationarity of stochastic processes is required in a variety of signal processing applications. When dealing with real-world problems, the presence of outliers and impulsive (heavy-tailed) noise causes classical stationarity tests to break down. In this work, a set of robust stationarity tests that are based on a sphericity statistic test (SST) in the frequency domain is proposed. Different possible approaches are investigated and compared to existing robust and non-robust stationarity tests in terms of the receiver operating characteristic (ROC). In addition to extensive simulations, a real-world data example of a malfunctioning window regulator motor, for which the dominant frequencies show a modulating character that results in a non-stationary signal, is investigated. Both for simulated and real-world data, the proposed methods significantly outperform existing approaches. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICASSP.2014.6854244 | Acoustics, Speech and Signal Processing |
Keywords | Field | DocType |
frequency-domain analysis,impulse noise,sensitivity analysis,signal processing,statistical testing,stochastic processes,ROC,SST,frequency domain sphericity statistic test,impulsive noise,outliers,receiver operating characteris- tic,robust stationarity testing,signal processing applications,stochastic processes,window regulator motor,Ivies timator,KPSS,SST,hypothesis testing,robustness,stationarity | Frequency domain,Signal processing,Receiver operating characteristic,Statistic,Pattern recognition,Sphericity,Computer science,Stochastic process,Outlier,Artificial intelligence | Conference |
ISSN | Citations | PageRank |
1520-6149 | 1 | 0.39 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jack Dagdagan | 1 | 1 | 0.39 |
| Michael Muma | 2 | 144 | 19.51 |
| Abdelhak M. Zoubir | 3 | 1036 | 148.03 |