Learn More
A method is presented for estimating the cross-spectral density of a hybrid process involving a time series and a point process. The method is based on the generalized cross-periodogram statistic, which is smoothed by splitting the whole record of the data into a number of disjoint subrecords. Estimates of the coherence function and the cross-covariance(More)
We consider estimates of certain time-domain parameters of a bivariate stationary-point process based on modified periodogram statistics. These estimates are shown to be asymptotically normal under regularity conditions. In the computations of the estimates, the advantage of using the FFT algorithm is demonstrated. Three examples, obtained by analyzing two(More)
Different statistical methods for the assessment of potential risk factors are discussed, in the case of a complex neurophysiological system, involving binary observations. The first approach describes the use of non-parametric methods, which are based on the cross-product ratio (CPR). The estimates of the CPR are given both in time and frequency domains(More)
In this paper, we provide a semi-parametric test for the hypothesis that the spectra of two stationary point processes (SPPs) are the same. The estimates of the second-order spectral density functions of the SPPs are obtained by using two different approaches: (a) by smoothing the modified periodogram statistics using a moving average weighting scheme, (b)(More)
In this work we apply spectral analysis techniques of bivariate stationary point processes for the estimation of the cross-correlation (CC). This is used for the study of a component of the neurophysiological system called muscle spindle. We are interested in the effect of different stimuli to the function of the muscle spindle by recording the response(More)
  • 1