A periodogram-based test for weak stationarity and consistency between sections in time series
D M Halliday1, J R Rosenberg, A Rigas
1Department of Electronics, University of York, York YO105DD, UK. dh20@ohm.york.ac.uk
Journal of Neuroscience Methods
|May 12, 2009
Summary
This study introduces the Periodogram Coefficient of Variation (PCOV) test to assess weak stationarity in spectral analysis. The PCOV test checks periodogram consistency across data sections, aiding time series analysis.
Area of Science:
- Signal Processing
- Time Series Analysis
- Statistical Inference
Background:
- Spectral estimation often assumes weak stationarity.
- Averaging periodograms across data sections or trials is a common estimation method.
- Assessing weak stationarity is crucial for the validity of spectral estimation results.
Purpose of the Study:
- To introduce a novel frequency domain test for assessing weak stationarity.
- To evaluate the Periodogram Coefficient of Variation (PCOV) test's effectiveness.
- To demonstrate the PCOV test's utility in exploratory time series analysis.
Main Methods:
- Developed the Periodogram Coefficient of Variation (PCOV) test.
- The PCOV test assesses consistency of periodogram ordinates across data sections.
- Applied the test to simulated, EMG, physiological tremor, and EEG data.
Main Results:
- The PCOV test provides a straightforward method for checking weak stationarity.
- The test demonstrated consistency across various simulated and experimental datasets.
- The PCOV test proved effective in assessing spectral parameter validity.
Conclusions:
- The PCOV test is a valuable tool for validating weak stationarity assumptions in spectral analysis.
- The test aids in the reliable estimation of spectral parameters.
- The PCOV test serves as a useful component of exploratory time series analysis.
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