Keywords: Cox point process; filtering; spatio-temporal process
@article{KYB_2009_45_6_a2,
author = {Frcalov\'a, Bla\v{z}ena and Bene\v{s}, Viktor},
title = {Spatio-temporal modelling of a {Cox} point process sampled by a curve, filtering and {Inference}},
journal = {Kybernetika},
pages = {912--930},
year = {2009},
volume = {45},
number = {6},
mrnumber = {2650073},
zbl = {1192.60073},
language = {en},
url = {http://geodesic.mathdoc.fr/item/KYB_2009_45_6_a2/}
}
Frcalová, Blažena; Beneš, Viktor. Spatio-temporal modelling of a Cox point process sampled by a curve, filtering and Inference. Kybernetika, Tome 45 (2009) no. 6, pp. 912-930. http://geodesic.mathdoc.fr/item/KYB_2009_45_6_a2/
[1] A. Baddeley, R. Turner, J. Møller, and M. Hazelton: Residual analysis for spatial point processes (with discussion). J. Royal Stat. Soc. B 67 (2005), 617–666. | MR
[2] O. Barndorff-Nielsen and N. Shephard: Non-Gaussian Ornstein–Uhlenbeck based models and some of their uses in financial economics. J. Royal Stat. Soc. B 63 (2001), 167–241. | MR
[3] O. Barndorff-Nielsen and J. Schmiegel: Lévy based tempo-spatial modelling; with applications to turbulence. Usp. Mat. Nauk 159 (2004), 63–90. | MR
[4] V. Beneš and B. Frcalová: Modelling and simulation of a neurophysiological experiment by spatio-temporal point processes. Image Anal. Stereol. 1 (2008), 27, 47–52.
[5] P. Brémaud: Point Process and Queues. Springer, New York 1981. | MR
[6] A. Brix and J. Møller: Space-time multi type log Gaussian Cox processes with a view to modelling weed data. Scand. J. Statist. 28 (2002), 471–488. | MR
[7] A. Brix and P. Diggle: Spatio-temporal prediction for log-Gaussian Cox processes. J. Royal Statist. Soc. B 63 (2001), 823–841. | MR
[8] M. A. Clyde and R. L. Wolpert: Nonparametric function estimation using overcomplete dictionaries. Bayesian Statistics 8 (2007), 1–24. | MR
[9] R. Cont and P. Tankov: Financial Modelling with Jump Processes. Chapman and Hall/CRC, Boca Raton 2004. | MR
[10] D. Daley and D. Vere-Jones: An Introduction to the Theory of Point Processes I, II. Springer, New York 2003, 2008. | MR
[11] A. Ergun, R. Barbieri, U. T. Eden, M. A. Wilson, and E. N. Brown: Construction of point process adaptive filter algorithms for neural systems using sequential Monte Carlo methods. IEEE Trans. Biomed. Engrg. 54 (2007), 3, 307–326.
[12] P. M. Fishman and D. Snyder: The statistical analysis of space-time point processes. IEEE Trans. Inform. Theory 22 (1976), 257–274. | MR
[13] G. Hellmund, M. Prokešová, and E. Vedel Jensen: Lévy based Cox point processes. Adv. Appl. Probab. 40 (2008), 3, 603–629. | MR
[14] M. Jacobsen: Point Processes Theory and Applications. Marked Point and Piecewise Deterministic Processes. Birkhäuser, Boston 2006. | MR
[15] A. F. Karr: Point Processes and Their Statistical Inference. Marcel Dekker, New York 1985. | MR
[16] P. Lánský and J. Vaillant: Stochastic model of the overdispersion in the place cell discharge. BioSystems 58 (2000), 27–32.
[17] R. Lechnerová, K. Helisová, and V. Beneš: Cox point processes driven by Ornstein–Uhlenbeck type processes. Method. Comp. Appl. Probab. 10 (2008), 3, 315–336.
[18] J. Møller and R. Waagepetersen: Statistics and Simulations of Spatial Point Processes. World Sci., Singapore 2003.
[19] J. Møller and C. Diaz-Avalos: Structured spatio-temporal shot-noise Cox point process models, with a view to modelling forest fires. Scand. J. Statist. (2009), to appear. | MR
[20] Y. Ogata: Space-time point process models for eartquake occurences. Ann. Inst. Statist. Math. 50 (1998), 379–402.
[21] J. Pedersen: The Lévy–Ito Decomposition of Independently Scattered Random Measure. Res. Report 2, MaPhySto, University of Aarhus 2003.
[22] R. D. Peng, F. P. Schoenberg, and J. Woods: A space-time conditional intensity model for evaluating a wildfire hazard risk. J. Amer. Statist. Assoc. 100 (2005), 469, 26–35. | MR
[23] B. S. Rajput and J. Rosinski: Spectral representations of infinitely divisible processes. Probab. Theory Related Fields 82 (1989), 451–487. | MR
[24] G. Roberts, O. Papaspiliopoulos, and P. Dellaportas: Bayesian inference for non-Gaussian Ornstein–Uhlenbeck stochastic volatility processes. J. Royal Statist. Soc. B 66 (2004), 369–393. | MR
[25] D. L. Snyder: Filtering and detection for doubly stochastic Poisson processes. IEEE Trans. Inform. Theory 18 (1972), 91–102. | MR | Zbl
[26] F. P. Schoenberg, D. R. Brillinger, and P. M. Guttorp: Point processes, spatial-temporal. In: Encycl. of Environmetrics (A. El. Shaarawi and W. Piegorsch, eds.), Vol. 3, Wiley, New York 2003, pp. 1573–1577.
[27] J. Zhuang: Second-order residual analysis of spatiotemporal point processes and applications in model evaluation. J. Royal Statist. Soc. B 68 (2006), 635–653. | MR | Zbl