Maximum likelihood estimation of Hawkes' self-exciting point processes

Summary

A maximum likelihood estimation procedure of Hawkes' self-exciting point process model is proposed with explicit presentations of the log-likelihood of the model and its gradient and Hessian. A simulation method of the process is also presented. Some numerical results are given.

This is a preview of subscription content, log in to check access.

References

  1. [1]

    Davidon, W. C. (1959). Variable metric method for minimization,Argonne Natl. Lab. Report, No. 5990.

  2. [2]

    Fletcher, R. and Powell, M. J. D. (1963). A rapidly convergent descent method for minimization,Computer J.,6, 163–168.

    MathSciNet  Article  Google Scholar 

  3. [3]

    Hawkes, A. G. (1971a). Spectra of self-exciting and mutually exciting point processes,Biometrika,58, 83–90.

    MathSciNet  Article  Google Scholar 

  4. [4]

    Hawkes, A. G. (1971b). Point spectra of some mutually exciting point processes,J. R. Statist. Soc., B,33, 438–443.

    MathSciNet  MATH  Google Scholar 

  5. [5]

    Hawkes, A. G. and Oakes, D. A. (1974). A cluster process representation of a self-exciting process,J. Appl. Prob.,11, 493–503.

    MathSciNet  Article  Google Scholar 

  6. [6]

    Hooke, R. and Jeeves, T. A. (1961). Direct search solution of numerical and statistical problems,J. Ass. Compt. Mach.,8, 212–229.

    Article  Google Scholar 

  7. [7]

    Lewis, P. A. W. (1970). Remarks on the theory, computation and application of the spectral analysis of series of events,J. Sound Vid.,12, 353–375.

    Article  Google Scholar 

  8. [8]

    Powell, M. J. D. (1963). An efficient method of finding the minimum of a function of several variables without calculating derivatives,Computer. J.,7, 155–162.

    MathSciNet  Article  Google Scholar 

  9. [9]

    Rosenbrock, H. H. (1960). An automatic method for finding the greatest or the least value of a function,Computer J.,3, 175–184.

    MathSciNet  Article  Google Scholar 

  10. [10]

    Rubin, I. (1972). Regular point processes and their detection,IEEE Trans. Inf. Theory, IT-18, 547–557.

    MathSciNet  Article  Google Scholar 

  11. [11]

    Segal, A. (1976). Recursive estimation from discrete-time point processes,IEEE Trans. Inf. Theory, IT-22, 422–431.

    MathSciNet  Article  Google Scholar 

  12. [12]

    Snyder, D. L. (1972). Smoothing for doubly stochastic Poisson processes,IEEE Trans. Inf. Theory, IT-18, 558–562.

    MathSciNet  Article  Google Scholar 

  13. [13]

    Vere-Jones, D. (1975). On updating algorithms and inference for stochastic point processes,M. S. Bartlett Memorial Volume.

    MathSciNet  Article  Google Scholar 

Download references

Authors

Additional information

The Institute of Statistical Mathematics

About this article

Cite this article

Ozaki, T. Maximum likelihood estimation of Hawkes' self-exciting point processes. Ann Inst Stat Math 31, 145–155 (1979). https://doi.org/10.1007/BF02480272

Download citation

Keywords

  • Point Process
  • Conjugate Gradient Method
  • Intensity Process
  • Uniform Random Number
  • Point Process Model