Covariance density estimation for autoregressive spectral modelling of point processes (Q1120240)

This paper discusses methods for the estimation of the covariance density and conditional intensity function of point processes and presents alternative computational efficient estimation algorithms leading always to positive semidefinite estimates, therefore adequate for autoregressive spectral analysis. Autoregressive spectral modelling of point processes from Yule-Walker type equations and Levinson recursion combined with the minimum AIC or CAT principle is illustrated with neurobiological data.