Abstract
First we put together basic definitions and fundamental facts and results from the theory of (un)marked point processes defined on Euclidean spaces \({\mathbb{R}}^{d}\). We introduce the notion random marked point process together with the concept of Palm distributions in a rigorous way followed by the definitions of factorial moment and cumulant measures and characteristics related with them. In the second part we define a variety of estimators of second-order characteristics and other so-called summary statistics of stationary point processes based on observations on a “convex averaging sequence” of windows \(\{W_{n},\,n \in \mathbb{N}\}\). Although all these (mostly edge-corrected) estimators make sense for fixed bounded windows our main issue is to study their behaviour when W n grows unboundedly as n → ∞. The first problem of large-domain statistics is to find conditions ensuring strong or at least mean-square consistency as n → ∞ under ergodicity or other mild mixing conditions put on the underlying point process. The third part contains weak convergence results obtained by exhausting strong mixing conditions or even m-dependence of spatial random fields generated by Poisson-based point processes. To illustrate the usefulness of asymptotic methods we give two Kolmogorov–Smirnov-type tests based on K-functions to check complete spatial randomness of a given point pattern in \({\mathbb{R}}^{d}\).
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References
Baddeley, A.: Fundamentals of point process statistics. In: Baddeley, A.J., Bárány, I., Schneider, R., Weil, W. (eds.) Stochastic Geometry - Lecture Notes in Mathematics, vol. 1892. Springer, Berlin (2007)
Billingsley, P.: Convergence of Probability Measures, 2nd edn. Wiley Series in Probability and Statistics: Probability and Statistics. Wiley, New York (1999)
Bolthausen, E.: On the central limit theorem for stationary mixing random fields. Ann. Probab. 10, 1047–1050 (1982)
Bradley, R.C.: Introduction to Strong Mixing Conditions. Vol. 1, 2, 3. Kendrick Press, Heber City (2007)
Bulinski, A.V., Shiryaev, A.N.: Theory of Stochastic Processes. FIZMATLIT, Moscow (2005) (in Russian)
Daley, D.J., Vere-Jones, D.: An introduction to the theory of point processes. Vol. I and II. Probab. Appl. (New York). Springer, New York (2003/2008)
Davydov, Y.A.: Convergence of distributions generated by stationary stochastic processes. Theor. Probab. Appl. 13, 691–696 (1968)
Götze, F., Hipp, C.: Asymptotic expansions for potential functions of i.i.d. random fields. Probab. Theor. Relat. Fields 82, 349–370 (1989)
Heinrich, L.: Asymptotic expansions in the central limit theorem for a special class of m-dependent random fields. I/II. Math. Nachr. 134, 83–106 (1987); 145, 309–327 (1990)
Heinrich, L.: Asymptotic Gaussianity of some estimators for reduced factorial moment measures and product densities of stationary Poisson cluster processes. Statistics 19, 87–106 (1988)
Heinrich, L.: Goodness-of-fit tests for the second moment function of a stationary multidimensional Poisson process. Statistics 22, 245–268 (1991)
Heinrich, L.: On existence and mixing properties of germ-grain models. Statistics 23, 271–286 (1992)
Heinrich, L.: Normal approximation for some mean-value estimates of absolutely regular tessellations. Math. Meth. Stat. 3, 1–24 (1994)
Heinrich, L.: Gaussian limits of multiparameter empirical K-functions of spatial Poisson processes (submitted) (2012)
Heinrich, L., Klein, S.: Central limit theorem for the integrated squared error of the empirical second-order product density and goodness-of-fit tests for stationary point processes. Stat. Risk Model. 28, 359–387 (2011)
Heinrich, L., Klein, S., Moser, M.: Empirical mark covariance and product density function of stationary marked point processes - A survey on asymptotic results. Meth. Comput. Appl. Probab. (2012), online available via DOI 10.1007/s11009-012-9314-7
Heinrich, L., Liebscher, E.: Strong convergence of kernel estimators for product densities of absolutely regular point processes. J. Nonparametr. Stat. 8, 65–96 (1997)
Heinrich, L., Lück, S., Schmidt, V.: Asymptotic goodness-of-fit tests for the Palm mark distribution of stationary point processes with correlated marks. Submitted (2012). An extended version is available at http://arxiv.org/abs/1205.5044
Heinrich, L., Molchanov, I.S.: Central limit theorem for a class of random measures associated with germ-grain models. Adv. Appl. Probab. 31, 283–314 (1999)
Heinrich, L., Pawlas, Z.: Weak and strong convergence of empirical distribution functions from germ-grain processes. Statistics 42, 49–65 (2008)
Heinrich, L., Prokešová, M.: On estimating the asymptotic variance of stationary point processes. Meth. Comput. Appl. Probab. 12, 451–471 (2010)
Illian, J., Penttinen, A., Stoyan, D., Stoyan, H.: Statistical Analysis and Modelling of Spatial Point Patterns. Wiley, Chichester (2008)
Ivanoff, G.: Central limit theorems for point processes. Stoch. Process. Appl. 12, 171–186 (1982)
Jolivet, E.: Central limit theorem and convergence of empirical processes for stationary point processes. In: Bartfai, P., Tomko, J. (eds.) Point Processes and Queueing Problems. North-Holland, Amsterdam (1980)
Jolivet, E.: Upper bound of the speed of convergence of moment density estimators for stationary point processes. Metrika 31, 349–360 (1984)
Karr, A.F.: Point Processes and their Statistical Inference. Dekker, New York (1986)
Matthes, K., Kerstan, J., Mecke, J.: Infinitely Divisible Point Processes. Wiley, Chichester (1978)
Molchanov, I.S.: Statistics of the Boolean Model for Practitioners and Mathematicians. Wiley, Chichester (1997)
Nguyen, X.X., Zessin, H.: Punktprozesse mit Wechselwirkung. Z. Wahrscheinlichkeitstheorie und verw. Gebiete 37, 91–126 (1976)
Ohser, J., Stoyan, D.: On the second-order and orientation analysis of planar stationary point processes. Biom. J. 23, 523–533 (1981)
Pawlas, Z.: Empirical distributions in marked point processes. Stoch. Process. Appl. 119, 4194–4209 (2009)
Rio, E.: Covariance inequality for strongly mixing processes. Ann. Inst. H. Poincaré Probab. Stat. 29, 587–597 (1993)
Ripley, B.D.: The second-order analysis of stationary point processes. J. Appl. Probab. 13, 255–266 (1976)
Schlather, M.: On the second-order characteristics of marked point processes. Bernoulli 7, 99–117 (2001)
Stoyan, D.: Fundamentals of point process statistics. In: Baddeley, A., Gregori, P., Mateu, J., Stoica, R., Stoyan, D. (eds.) Case Studies in Spatial Point Process Modeling. Lecture Notes in Statistics, vol. 185. Springer, New York (2006)
Stoyan, D., Kendall, W., Mecke, J.: Stochastic Geometry and Its Applications, 2nd edn. Wiley, New York (1995)
Wills, J.M.: Zum Verhältnis von Volumen und Oberfläche bei konvexen Körpern. Archiv der Mathematik 21, 557–560 (1970)
Yoshihara, K.: Limiting behavior of U-statistics for stationary, absolutely regular processes. Z. Wahrscheinlichkeitstheorie und verw. Gebiete 35, 237–252 (1976)
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Heinrich, L. (2013). Asymptotic Methods in Statistics of Random Point Processes. In: Spodarev, E. (eds) Stochastic Geometry, Spatial Statistics and Random Fields. Lecture Notes in Mathematics, vol 2068. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33305-7_4
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DOI: https://doi.org/10.1007/978-3-642-33305-7_4
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