Statistical Analysis and Modelling of Spatial Point PatternsJohn Wiley & Sons, 15 avr. 2008 - 560 pages Spatial point processes are mathematical models used to describe and analyse the geometrical structure of patterns formed by objects that are irregularly or randomly distributed in one-, two- or three-dimensional space. Examples include locations of trees in a forest, blood particles on a glass plate, galaxies in the universe, and particle centres in samples of material. Numerous aspects of the nature of a specific spatial point pattern may be described using the appropriate statistical methods. Statistical Analysis and Modelling of Spatial Point Patterns provides a practical guide to the use of these specialised methods. The application-oriented approach helps demonstrate the benefits of this increasingly popular branch of statistics to a broad audience. The book:
Statistical Analysis and Modelling of Spatial Point Patterns is ideally suited for researchers in the many areas of application, including environmental statistics, ecology, physics, materials science, geostatistics, and biology. It is also suitable for students of statistics, mathematics, computer science, biology and geoinformatics. |
Table des matières
| 1 | |
2 The homogeneous Poisson point process | 57 |
3 Finite point processes | 99 |
4 Stationary point processes | 173 |
5 Stationary marked point processes | 293 |
6 Modelling and simulation of stationary point processes | 363 |
7 Fitting and testing point process models | 445 |
Appendix A Fundamentals of statistics | 479 |
Appendix B Geometrical characteristics of sets | 483 |
Appendix C Fundamentals of geostatistics | 489 |
| 493 | |
| 515 | |
| 519 | |
| 527 | |
Autres éditions - Tout afficher
Statistical Analysis and Modelling of Spatial Point Patterns Janine Illian,Antti Penttinen,Helga Stoyan,Dietrich Stoyan Aucun aperçu disponible - 2008 |
Statistical Analysis and Modelling of Spatial Point Patterns Dr. Janine Illian,Prof. Antti Penttinen,Dr. Helga Stoyan,Dietrich Stoyan Aucun aperçu disponible - 2008 |
Expressions et termes fréquents
algorithm amacrine cells applied approach bandwidth behaviour centres cluster process considered context corresponding covariance Cox process defined denoted density function described disc edge-correction empirical example Figure finite point processes formulas geostatistical Gibbs processes Gold particles hard-core process homogeneous Poisson process indices inhomogeneous intensity function inter-point distance interaction isotropic K-function likelihood function locations mark correlation function marked point process maximum likelihood mean number nearest nearest-neighbour distance d.f. neighbour number of points observation window pair correlation function pair potential pairs of points parameters planar point density point pattern point process models point process statistics points of type points xi Poisson distribution probability density function product density qualitative marks radius random field random number random variable rmax sample second-order characteristics shows simulation spatial point sphere stationary point processes stationary processes Stoyan Strauss process summary characteristics tessellation trees typical point values variance variogram Voronoi tessellation x1 xn yields