Statistical Science

Comment on “A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications” by Alex Reinhart

Frederic Paik Schoenberg

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Article information

Source
Statist. Sci., Volume 33, Number 3 (2018), 325-326.

Dates
First available in Project Euclid: 13 August 2018

Permanent link to this document
https://projecteuclid.org/euclid.ss/1534147224

Digital Object Identifier
doi:10.1214/18-STS652

Mathematical Reviews number (MathSciNet)
MR3843377

Zentralblatt MATH identifier
06991121

Citation

Schoenberg, Frederic Paik. Comment on “A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications” by Alex Reinhart. Statist. Sci. 33 (2018), no. 3, 325--326. doi:10.1214/18-STS652. https://projecteuclid.org/euclid.ss/1534147224


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References

  • Adelfio, G. and Schoenberg, F. P. (2009). Point process diagnostics based on weighted second-order statistics and their asymptotic properties. Ann. Inst. Statist. Math. 61 929–948.
  • Baddeley, A., Turner, R., Møller, J. and Hazelton, M. (2005). Residual analysis for spatial point processes. J. R. Stat. Soc. Ser. B. Stat. Methodol. 67 617–666.
  • Cronie, O. and van Lieshout, M. N. M. (2016). Bandwidth selection for kernel estimators of the spatial intensity function. Available at arXiv:1611.10221.
  • Diggle, P. J. (2014). Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, 3rd ed. Monographs on Statistics and Applied Probability 128. CRC Press, Boca Raton, FL.
  • Harte, D. S. (2013). Bias in fitting the ETAS model: A case study based on New Zealand seismicity. Geophys. J. Int. 192 390–412.
  • Schoenberg, F. P. (2013). Facilitated estimation of ETAS. Bull. Seismol. Soc. Am. 103 601–605.
  • Veen, A. and Schoenberg, F. P. (2008). Estimation of space-time branching process models in seismology using an EM-type algorithm. J. Amer. Statist. Assoc. 103 614–624.

See also

  • Main article: A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications.