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Volker Schmidt

Researcher at University of Ulm

Publications -  353
Citations -  8559

Volker Schmidt is an academic researcher from University of Ulm. The author has contributed to research in topics: Point process & Stochastic modelling. The author has an hindex of 39, co-authored 331 publications receiving 7281 citations. Previous affiliations of Volker Schmidt include Charles University in Prague.

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An investigation of the spatial correlations for relative purchasing power in Baden–Württemberg

TL;DR: In this paper, the authors investigated the spatial correlations for relative purchasing power of the townships in Baden-Wurttemberg and found that there are positive correlations for small distances between different townships but that these positive correlations become weaker over the years until they are almost nonexistent.
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Asymptotic properties of Euclidean shortest-path trees in random geometric graphs

TL;DR: Asymptotic properties of two functionals on Euclidean shortest-path trees appearing in random geometric graphs in R2 which can be used, for example, as models for fixed-access telecommunication networks are considered.
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Limit theorems for functionals on the facets of stationary random tessellations

TL;DR: In this article, the authors derived the total volume of manifold processes induced by stationary random tessellations in a convex sampling window that expands unboundedly and showed that all of the total volumes measured in the sampling window are approximately normally distributed when the sample size is sufficiently large.

Stochastic inequalities between customer-stationary and time-stationary characteristics of queueing

TL;DR: By means of a general intensity conservation principle for stationary processes with imbedded marked point processes (PMP) stochastic inequalities are proved between customer-stationary and time-stationarial characteristics of queueing systems GIG/sIr as mentioned in this paper.
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Modelling tree roots in mixed forest stands by inhomogeneous marked Gibbs point processes.

TL;DR: A new approach to parametric inference when the data are inhomogeneous replicated marked point patterns is proposed, which combines inhomogeneity, marks and interaction between the marked points.