Source: Adv. in Appl. Probab. Volume 37, Number 3
(2005), 629-646.
Our objective is to construct a perfect simulation algorithm for unmarked and marked Hawkes processes. The usual
straightforward simulation algorithm suffers from edge effects, whereas our perfect simulation algorithm does not. By
viewing Hawkes processes as Poisson cluster processes and using their branching and conditional independence
structures, useful approximations of the distribution function for the length of a cluster are derived. This is used to
construct upper and lower processes for the perfect simulation algorithm. A tail-lightness condition turns out to be of
importance for the applicability of the perfect simulation algorithm. Examples of applications and empirical results
are presented.
References
Apostol, T. M. (1974). Mathematical Analysis. Addison-Wesley, Reading, MA.
Mathematical Reviews (MathSciNet):
MR344384
Asmussen, S. (1987). Applied Probability and Queues. John Wiley, Chichester.
Mathematical Reviews (MathSciNet):
MR889893
Brémaud, P. and Massoulié, L. (1996). Stability of nonlinear Hawkes processes. Ann. Prob. 24, 1563--1588.
Brémaud, P. and Massoulié, L. (2001). Hawkes branching point processes without ancestors. J. Appl. Prob. 38, 122--135.
Brémaud, P., Nappo, G. and Torrisi, G. (2002). Rate of convergence to equilibrium of marked Hawkes processes. J. Appl. Prob. 39, 123--136.
Brix, A. and Kendall, W. S. (2002). Simulation of cluster point processes without edge effects. Adv. Appl. Prob. 34, 267--280.
Chornoboy, E. S., Schramm, L. P. and Karr, A. F. (1988). Maximum likelihood identification of neural point process systems. Biol. Cybernet. 59, 265--275.
Mathematical Reviews (MathSciNet):
MR961117
Daley, D. J. and Vere-Jones, D. (2003). An Introduction to the Theory of Point Processes, Vol. 1, Elementary Theory and Methods, 2nd edn. Springer, New York.
Dwass, M. (1969). The total progeny in a branching process and a related random walk. J. Appl. Prob. 6, 682--686.
Mathematical Reviews (MathSciNet):
MR253433
Hawkes, A. G. (1971). Point spectra of some mutually exciting point processes. J. R. Statist. Soc. B 33, 438--443.
Mathematical Reviews (MathSciNet):
MR358976
Hawkes, A. G. (1971). Spectra of some self-exciting and mutually exciting point processes. Biometrika 58, 83--90.
Mathematical Reviews (MathSciNet):
MR278410
Hawkes, A. G. (1972). Spectra of some mutually exciting point processes with associated variables. In Stochastic Point Processes, ed. P. A. W. Lewis, John Wiley, New York, pp. 261--271.
Mathematical Reviews (MathSciNet):
MR358977
Hawkes, A. G. and Adamopoulos, L. (1973). Cluster models for earthquakes -- regional comparisons. Bull. Internat. Statist. Inst. 45, 454--461.
Hawkes, A. G. and Oakes, D. (1974). A cluster representation of a self-exciting process. J. Appl. Prob. 11, 493--503.
Mathematical Reviews (MathSciNet):
MR378093
Jagers, P. (1975). Branching Processes with Biological Applications. John Wiley, London.
Mathematical Reviews (MathSciNet):
MR488341
Kendall, W. S. and Møller, J. (2000). Perfect simulation using dominating processes on ordered spaces, with application to locally stable point processes. Adv. Appl. Prob. 32, 844--865.
Møller, J. (2003). Shot noise Cox processes. Adv. Appl. Prob. 35, 614--640.
Møller, J. and Rasmussen, J. G. (2005). Approximate simulation of Hawkes processes. Submitted.
Møller, J. and Torrisi, G. L. (2005). Generalised shot noise Cox processes. Adv. Appl. Prob. 37, 48--74.
Møller, J. and Torrisi, G. L. (2005). Perfect and approximate simulation of spatial Hawkes processes. In preparation.
Møller, J. and Waagepetersen, R. P. (2004). Statistical Inference and Simulation for Spatial Point Processes. Chapman and Hall, Boca Raton, FL.
Ogata, Y. (1988). Statistical models for earthquake occurrences and residual analysis for point processes. J. Amer. Statist. Assoc. 83, 9--27.
Ogata, Y. (1998). Space-time point-process models for earthquake occurrences. Ann. Inst. Statist. Math. 50, 379--402.
Propp, J. G. and Wilson, D. B. (1996). Exact sampling with coupled Markov chains and applications to statistical mechanics. Random Structures Algorithms 9, 223--252.
Ripley, B. D. (1987). Stochastic Simulation. John Wiley, New York.
Mathematical Reviews (MathSciNet):
MR875224
Rudin, W. (1987). Real and Complex Analysis. McGraw-Hill, New York.
Mathematical Reviews (MathSciNet):
MR924157
Vere-Jones, D. and Ozaki, T. (1982). Some examples of statistical inference applied to earthquake data. Ann. Inst. Statist. Math. 34, 189--207.