Semiparametric estimation regression models for point processes based on one realization

We are dealing with regression models for point processes having a multiplicative intensity process of the form #alpha#(t) x b_t. The deterministic function #alpha# describes the long-term trend of the process. The stochastic process b accounts for the short-term random variations and depends on a finite-dimensional parameter. The semiparametric estimation procedure is based on one single observation over a long time interval. We will use penalized estimation functions to estimate the trend #alpha#, while the likelihood approach to point processes is employed for the parametric part of the problem. Our methods are applied to earthquake data as well as to records on 24-hours ECG. (orig.)Available from TIB Hannover: RR 6137(66) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman