Rectification of the Bias in the Wavelet Power Spectrum

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Rotation and magnetism of Kepler pulsating solar-like stars : Towards asteroseismically calibrated age-rotation relations (2014, methodology): “The first study we performed to measure the surface rotation was a time-period analysis using a wavelet decomposition (Torrence & Compo 1998), improved for the low-frequency region as in Liu et al. (2007), and adapted to our asteroseismic purposes following Mathur et al. (2010b).”
Geophysical Applications of Partial Wavelet Coherence and Multiple Wavelet Coherence (2012, methodology): “(2004)] that rectified the bias in the wavelet power spectrum (WPS) and wavelet crossspectrum (Liu et al. 2007; Veleda et al. 2012).”
Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review (2019, methodology): “In finance, the DWT, MODWT and XWT are the principal used types [52,59], where the main advantage is the ability to conserve time information.”
Analyzing time–frequency relationship between oil price and exchange rate in Pakistan through wavelets (2015, unknown): “The cross-wavelet transform helps in this regard, but we found that WPS provide biased results due to the occurrence of lowfrequency oscillation in PWS [32,51].”