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Stein's method and point process approximation

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  • Barbour, A. D.
  • Brown, T. C.

Abstract

The Stein-Chen method for Poisson approximation is adapted into a form suitable for obtaining error estimates for the approximation of the whole distribution of a point process on a suitable topological space by that of a Poisson process. The adaptation involves consideration of an immigration-death process on the topological space, whose equilibrium distribution is that of the approximating Poisson process; the Stein equation has a simple interpretation in terms of the generator of the immigration-death process. The error estimates for process approximation in total variation do not have the 'magic' Stein-Chein multiplying constants, which for univariate approximation tend to zero as the mean gets larger, but examples, including Bernoulli trials and the hard-core model on the torus, show that this is not possible. By choosing weaker metrics on the space of distributions of point processes, it is possible to reintroduce these constants. The proofs actually yield an improved estimate for one of the constants in the univariate case.

Suggested Citation

  • Barbour, A. D. & Brown, T. C., 1992. "Stein's method and point process approximation," Stochastic Processes and their Applications, Elsevier, vol. 43(1), pages 9-31, November.
    • Handle: RePEc:eee:spapps:v:43:y:1992:i:1:p:9-31
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    Citations

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    Cited by:

    1. Brown, Timothy C. & Weinberg, Graham V. & Xia, Aihua, 2000. "Removing logarithms from Poisson process error bounds," Stochastic Processes and their Applications, Elsevier, vol. 87(1), pages 149-165, May.
    2. Brown, Timothy C. & Xia, Aihua, 1995. "On Stein-Chen factors for Poisson approximation," Statistics & Probability Letters, Elsevier, vol. 23(4), pages 327-332, June.
    3. Phelan, Michael J., 1997. "Approach to stationarity for birth and death on flows," Stochastic Processes and their Applications, Elsevier, vol. 66(2), pages 183-207, March.
    4. Schuhmacher, Dominic, 2005. "Distance estimates for dependent superpositions of point processes," Stochastic Processes and their Applications, Elsevier, vol. 115(11), pages 1819-1837, November.
    5. Schulte, Matthias & Thäle, Christoph, 2012. "The scaling limit of Poisson-driven order statistics with applications in geometric probability," Stochastic Processes and their Applications, Elsevier, vol. 122(12), pages 4096-4120.
    6. Xia, Aihua & Zhang, Fuxi, 2008. "A polynomial birth-death point process approximation to the Bernoulli process," Stochastic Processes and their Applications, Elsevier, vol. 118(7), pages 1254-1263, July.
    7. He, Shengwu & Xia, Aihua, 1997. "On poisson approximation to the partial sum process of a Markov chain," Stochastic Processes and their Applications, Elsevier, vol. 68(1), pages 101-111, May.
    8. Gan, H.L. & Xia, A., 2015. "Stein’s method for conditional compound Poisson approximation," Statistics & Probability Letters, Elsevier, vol. 100(C), pages 19-26.

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