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On Rescaled Poisson Processes and the Brownian Bridge

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Abstract

The process obtained by rescaling a homogeneous Poisson process by the maximum likelihood estimate of its intensity is shown to have surprisingly strong self-correcting behavior. Formulas for the conditional intensity and moments of the rescaled Poisson process are derived, and its behavior is demonstrated using simulations. Relationships to the Brownian bridge are explored, and implications for point process residual analysis are discussed.

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References

  • Aalen, O. and Hoem, J. (1978). Random time changes for multivariate counting processes, Scand. Actuar. J., 17, 81–101.

    Google Scholar 

  • Abramowitz, M. (1964). Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables (eds. M. Abramowitz and I. Stegun), U. S. Government Printing Office, Washington D. C.

    Google Scholar 

  • Arsham, H. (1987). A modified one-sided K-S confidence region narrower in one tail, Comm. Statist. Theory Methods, 16, 17–28.

    Google Scholar 

  • Billingsley, P. (1968). Convergence of Probability Measures, Wiley, New York.

    Google Scholar 

  • Brémaud, P. (1972). A martingale approach to point processes, Memorandum ERL-M345, Electronics Research Laboratory, University of California, Berkeley.

    Google Scholar 

  • Bretagnolle, J. and Massart, P. (1989). Hungarian constructions from the nonasymptotic viewpoint, Ann. Probab., 17(1), 239–256.

    Google Scholar 

  • Brown, T. and Nair, M. (1988). Poisson approximations for time-changed point processes, Stoch. Process. Appl., 29, 247–256.

    Google Scholar 

  • Csörgő, M. and Horváth, L. (1992). Rényi-type empirical processes, J. Multivariate Anal., 41, 338–358.

    Google Scholar 

  • Daley, D. and Vere-Jones, D. (1988). An Introduction to the Theory of Point Processes, Springer, New York.

    Google Scholar 

  • Davies, R. (1977). Testing the hypothesis that a point process is Poisson, Adv. in Appl. Probab., 9, 724–746.

    Google Scholar 

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

    Google Scholar 

  • Heinrich, L. (1991). Goodness-of-fit tests for the second moment function of a stationary multidimensional Poisson process, Statistics, 22, 245–278.

    Google Scholar 

  • Isham, V. and Westcott, M. (1979). A self-correcting point process, Stochastic Process. Appl., 8, 335–347.

    Google Scholar 

  • Kac, M. (1949). On deviations between theoretical and empirical distributions, Proc. Nat. Acad. Sci. U.S.A., 35, 252–257.

    Google Scholar 

  • Karr, A. (1991). Point Processes and Their Statistical Inference, 2nd ed., Dekker, New York.

    Google Scholar 

  • Kutoyants, Y. (1984). On nonparametric estimation of intensity function of inhomogeneous Poisson process, Problems of Control and Information Theory, 13, 253–258.

    Google Scholar 

  • Lee, C. (1986). Estimation of the intensity of a Poisson process, Comm. Statist. Simulation Comput., 15, 747–759.

    Google Scholar 

  • Lisek, B. and Lisek, M. (1985). A new method for testing whether a point process is Poisson, Statistics, 16, 445–450.

    Google Scholar 

  • Major, P. (1990). A note on the approximation of the uniform empirical process, Ann. Probab., 18, 129–139.

    Google Scholar 

  • Merzbach, E. and Nualart, D. (1986). A characterization of the spatial Poisson process and changing time, Ann. Probab., 14, 1380–1390.

    Google Scholar 

  • Meyer, P. (1971). Démonstration simplifée d'un théorème de Knight, Lecture Notes in Math., Vol. 191, 191–195.

  • Nair, M. (1990). Random space change for multiparameter point processes, Ann. Probab., 18, 1222–1231.

    Google Scholar 

  • Ogata, Y. (1978). The asymptotic behavior of maximum likelihood estimators for stationary point processes, Ann. Inst. Statist. Math., 30, 243–262.

    Google Scholar 

  • Ogata, Y. (1988). Statistical model for earthquake occurrences and residual analysis for point processes, J. Amer. Statist. Assoc., 83, 9–27.

    Google Scholar 

  • Ogata, Y. and Vere-Jones, D. (1984). Inference for earthquake models: A self-correcting model, Stochastic Process. Appl., 17, 337–347.

    Google Scholar 

  • Papangelou, F. (1972). Integrability of expected increments of point processes and a related random change of scale, Trans. Amer. Math. Soc., 165, 483–506.

    Google Scholar 

  • Pollard, D. (1984). Convergence of Stochastic Processes, Springer, New York.

    Google Scholar 

  • Saw, J. (1975). Tests on the intensity of a Poisson process, Comm. Statist., 4, 777–782.

    Google Scholar 

  • Schoenberg, F. (1997). Assessment of multi-dimensional point process models, Ph.D. Thesis, Department of Statistics, University of California, Berkeley.

    Google Scholar 

  • Schoenberg, F. (1999). Transforming spatial point processes into Poisson processes, Stochastic Process. Appl., 81(2), 155–164.

    Google Scholar 

  • Solow, A. (1993). Model-checking in non-stationary Poisson processes, Appl. Stochastic Models Data Anal., 8, 129–132.

    Google Scholar 

  • Yokoyama, S., Miyawaki, N. and Sakamoto, H. (1993). On the estimation problem in the analysis of the time-dependent Poisson process, Comm. Statist. Simulation Comput., 22, 591–614.

    Google Scholar 

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Schoenberg, F.P. On Rescaled Poisson Processes and the Brownian Bridge. Annals of the Institute of Statistical Mathematics 54, 445–457 (2002). https://doi.org/10.1023/A:1022494523519

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