Earticle

To extract the brainstem auditory evoked potentials and remove the spontaneous electroencephalograph and other background noises, the paper proposed a new method of wavelet information entropy theory de-noising based on wavelet de-noising. In the method, the threshold function is improved by introducing the weighting factor and determine the weighting factor according to the wavelet information entropy to obtain a better de-noising effect. The simulation experiment shows, compared to the traditional wavelet de-noising method, the new method can preserve the details of the signal better and make the signal waveform smoother. At the same time, the new method can suppress the pulse signal, which make the system has a better de-noising performance and achieve higher quality extraction brainstem auditory evoked potentials.

A class of threshold-asymmetric GRACH(TGARCH, hereafter) models has been useful for explaining asymmetric volatilities in the field of financial time series. The cumulative impulse response function of a conditionally heteroscedastic time series often measures a degree of unstability in volatilities. In this article, a general form of the cumulative impulse response function of the TGARCH model is discussed. In particular, We present formula in their closed forms for the first two lower order models, viz., TGARCH(1, 1) and TGARCH(2, 2).