On Variograms in Point Process Statistics, II: Models of Markings and Ecological Interpretation

By Dietrich Stoyan, O. Wälder. 2000

50 citations recorded by the source corpus.

Subfields: Applied Mathematics, Environmental Engineering, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/3f47b849c2a7ccc87a2a6a34f7ea435e8cdbe90c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Mathematics

1. Jesper Møller
2. Rasmus Waagepetersen
3. Peter J. Diggle
4. Martin Schlather
5. M. V. Lieshout
6. Yongtao Guan
7. Jakob Gulddahl Rasmussen
8. Adrian Baddeley

In Environmental Engineering

1. Jesper Møller
2. Rasmus Waagepetersen
3. Peter J. Diggle
4. Martin Schlather
5. Paulo Justiniano Ribeiro Jr.
6. R. M. Lark
7. Yongtao Guan
8. Wolfgang Nowak

In Nature and Landscape Conservation

1. Jesper Møller
2. Rasmus Waagepetersen
3. Peter J. Diggle
4. François Rousset
5. Gilles Guillot

Citations in the corpus

• What mark variograms tell about spatial plant interactions (2013, unknown): “This shape is very different from the shape of a geostatistical variogram (see also Wälder and Stoyan, 1996; Stoyan and Wälder, 2000; Suzuki et al., 2008) reflecting negative autocor-”
• Modern Statistics for Spatial Point Processes * (2007, background): “The process ={(u, mu) : u ∈ X} is called a marked point process, and indeed marked point processes are important for many applications; see Stoyan & Stoyan (1995), Stoyan & Wälder (2000), Schlather et al. (2004), Schlather (2001), Møller & Waagepetersen (2003b).”
• Detecting dependence between marks and locations of marked point processes (2004, background): “Finally, Stoyan and Wälder (2000) introduced novel bivariate marked point processes where the marks distinguish between high and low growth rates only.”