Free Access
Issue
ESAIM: PS
Volume 17, 2013
Page(s) 767 - 788
DOI https://doi.org/10.1051/ps/2012027
Published online 04 November 2013
  1. A.J. Baddeley, M. Kerscher, K. Schladitz and B.T. Scott, Estimating the J function without edge correction. Research report of the department of mathematics, University of Western Australia (1997). [Google Scholar]
  2. J-M. Bardet, P. Doukhan, G. Lang and N. Ragache, Dependent Lindeberg central limit theorem and some applications. ESAIM: PS 12 (2008) 154–172. [CrossRef] [EDP Sciences] [Google Scholar]
  3. S. Bernstein, Quelques remarques sur le théorème limite Liapounoff. C.R. (Dokl.) Acad. Sci. URSS 24 (1939) 3–8. [Google Scholar]
  4. J.E. Besag, Comments on Ripley’s paper. J. Roy. Statist. Soc. Ser. B 39 (1977) 193–195. [Google Scholar]
  5. S.N. Chiu, Correction to Koen’s critical values in testing spatial randomness. J. Stat. Comput. Simul. 77 (2007) 1001–1004. [CrossRef] [Google Scholar]
  6. S.N. Chiu and K.I. Liu, Generalized Cramér-von Mises goodness-of-fit tests for multivariate distributions. Comput. Stat. Data Anal. 53 (2009) 3817–3834. [CrossRef] [Google Scholar]
  7. N.A. Cressie, Statistics for spatial data. John Wiley and Sons, New York (1993). [Google Scholar]
  8. P.J. Diggle, Statistical analysis of spatial point patterns. Academic Press, London (1983). [Google Scholar]
  9. M. Fromont, B. Laurent and P. Reynaud-Bouret, Adaptive tests of homogeneity for a Poisson process. Ann. I.H.P. (B) 47 (2011) 176–213. [Google Scholar]
  10. P. Grabarnik and S.N. Chiu, Goodness-of-fit test for complete spatial randomness against mixtures of regular and clustured spatial point processes. Biometrika 89 (2002) 411–421. [CrossRef] [Google Scholar]
  11. J. Gignoux, C. Duby and S. Barot, Comparing the performances of Diggle’s tests of spatial randomness for small samples with and without edge effect correction: application to ecological data. Biometrics 55 (1999) 156–164. [CrossRef] [PubMed] [Google Scholar]
  12. Y. Guan, On nonparametric variance estimation for second-order statistics of inhomogeneous spatial point Processes with a known parametric intensity form. J. Am. Stat. Ass. 104 (2009) 1482–1491. [CrossRef] [Google Scholar]
  13. L.P. Ho and S.N. Chiu, Testing Uniformity of a Spatial Point Pattern. J. Comput. Graph. Stat. 16 2 (2007) 378–398. [CrossRef] [Google Scholar]
  14. L. Heinrich, Goodness-of-fit tests for the second moment function of a stationary multidimensional Poisson process. Statistics 22 (1991) 245–268. [CrossRef] [Google Scholar]
  15. J. Illian, A. Penttinen, H. Stoyan and D. Stoyan, Statistical analysis and modelling of spatial point patterns. Wiley-Interscience, Chichester (2008). [Google Scholar]
  16. C. Koen, Approximate confidence bounds for Ripley’s statistic for random points in a square. Biom. J. 33 (1991) 173–177. [CrossRef] [Google Scholar]
  17. E. Marcon and F. Puech, Evaluating the geographic concentration of industries using distance-based methods. J. Econom. Geogr. 3 (2003) 409–428. [CrossRef] [Google Scholar]
  18. J. Møller and R.P. Waagepetersen, Statistical inference and simulation for spatial point processes, vol. 100 of Monographs on statistics and applied probability. Chapman and Hall/CRC, Boca Raton (2004). [Google Scholar]
  19. R Development Core Team (2012). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. http://www.R-project.org. [Google Scholar]
  20. B.D. Ripley, The second-order analysis of stationary point processes. J. Appl. Probab. 13 (1976) 255–266. [CrossRef] [Google Scholar]
  21. B.D. Ripley, Modelling spatial patterns. J. Roy. Statist. Soc. Ser. B 39 2 (1977) 172–212. [Google Scholar]
  22. B.D. Ripley, Tests of randomness for spatial point patterns. J. Roy. Statist. Soc. Ser. B 41 3 (1979) 368–374. [Google Scholar]
  23. B.D. Ripley, Spatial statistics. John Wiley and Sons, New York (1981). [Google Scholar]
  24. R. Saunders and G.M. Funk, Poisson limits for a clustering model of Strauss. J. Appl. Probab. 14 (1977) 776–784. [CrossRef] [Google Scholar]
  25. D. Stoyan, W.S. Kendall and J. Mecke, Stochastic geometry and its applications. Akademie-Verlag, Berlin (1987). [Google Scholar]
  26. D. Stoyan and H. Stoyan, Fractals, Random Shapes and Point Fields. Methods of Geometrical Statistics. John Wiley and Sons, New York (1994). [Google Scholar]
  27. C.C. Taylor, I.L. Dryden and R. Farnoosh, The K function for nearly regular point processes. Biometrics 57 (2000) 224–231. [CrossRef] [Google Scholar]
  28. M. Thomas, A generalization of Poisson’s binomial limit for use in ecology. Biometrika 36 (1949) 18–25. [MathSciNet] [PubMed] [Google Scholar]
  29. E. Thönnes and M.-C. van Lieshout, A comparative study on the power of van Lieshout and Baddeley’s J function. Biom. J. 41 (1999) 721–734. [CrossRef] [Google Scholar]
  30. J.S. Ward and F.J. Ferrandino, New derivation reduces bias and increases power of Ripley’s L index. Ecological Modelling 116 (1999) 225–236. [CrossRef] [Google Scholar]

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