A K-function for inhomogeneous random measures with geometric features

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Standard

A K-function for inhomogeneous random measures with geometric features. / Svane, Anne Marie; Stephensen, Hans Jacob Teglbjærg; Waagepetersen, Rasmus.

I: Spatial Statistics, Bind 51, 100656, 2022, s. 1-30.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Svane, AM, Stephensen, HJT & Waagepetersen, R 2022, 'A K-function for inhomogeneous random measures with geometric features', Spatial Statistics, bind 51, 100656, s. 1-30. https://doi.org/10.1016/j.spasta.2022.100656

APA

Svane, A. M., Stephensen, H. J. T., & Waagepetersen, R. (2022). A K-function for inhomogeneous random measures with geometric features. Spatial Statistics, 51, 1-30. [100656]. https://doi.org/10.1016/j.spasta.2022.100656

Vancouver

Svane AM, Stephensen HJT, Waagepetersen R. A K-function for inhomogeneous random measures with geometric features. Spatial Statistics. 2022;51:1-30. 100656. https://doi.org/10.1016/j.spasta.2022.100656

Author

Svane, Anne Marie ; Stephensen, Hans Jacob Teglbjærg ; Waagepetersen, Rasmus. / A K-function for inhomogeneous random measures with geometric features. I: Spatial Statistics. 2022 ; Bind 51. s. 1-30.

Bibtex

@article{c98ed2471de24698b866a6ec1aeb4b6b,
title = "A K-function for inhomogeneous random measures with geometric features",
abstract = "This paper introduces a K-function for assessing second-order properties of inhomogeneous random measures generated by marked point processes. The marks can be geometric objects like fibers or sets of positive volume, and the presented K-function takes into account geometric features of the marks, such as tangent directions of fibers. The K-function requires an estimate of the inhomogeneous density function of the random measure. We introduce parametric estimates for the density function based on parametric models that represent large scale features of the inhomogeneous random measure. The proposed methodology is applied to simulated fiber patterns as well as a three-dimensional dataset of steel fibers in concrete.",
keywords = "Fiber process, Inhomogeneous, K-function, Marked point process, Random measure, Tangent directions",
author = "Svane, {Anne Marie} and Stephensen, {Hans Jacob Teglbj{\ae}rg} and Rasmus Waagepetersen",
note = "Publisher Copyright: {\textcopyright} 2022 Elsevier B.V.",
year = "2022",
doi = "10.1016/j.spasta.2022.100656",
language = "English",
volume = "51",
pages = "1--30",
journal = "Spatial Statistics",
issn = "2211-6753",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - A K-function for inhomogeneous random measures with geometric features

AU - Svane, Anne Marie

AU - Stephensen, Hans Jacob Teglbjærg

AU - Waagepetersen, Rasmus

N1 - Publisher Copyright: © 2022 Elsevier B.V.

PY - 2022

Y1 - 2022

N2 - This paper introduces a K-function for assessing second-order properties of inhomogeneous random measures generated by marked point processes. The marks can be geometric objects like fibers or sets of positive volume, and the presented K-function takes into account geometric features of the marks, such as tangent directions of fibers. The K-function requires an estimate of the inhomogeneous density function of the random measure. We introduce parametric estimates for the density function based on parametric models that represent large scale features of the inhomogeneous random measure. The proposed methodology is applied to simulated fiber patterns as well as a three-dimensional dataset of steel fibers in concrete.

AB - This paper introduces a K-function for assessing second-order properties of inhomogeneous random measures generated by marked point processes. The marks can be geometric objects like fibers or sets of positive volume, and the presented K-function takes into account geometric features of the marks, such as tangent directions of fibers. The K-function requires an estimate of the inhomogeneous density function of the random measure. We introduce parametric estimates for the density function based on parametric models that represent large scale features of the inhomogeneous random measure. The proposed methodology is applied to simulated fiber patterns as well as a three-dimensional dataset of steel fibers in concrete.

KW - Fiber process

KW - Inhomogeneous

KW - K-function

KW - Marked point process

KW - Random measure

KW - Tangent directions

U2 - 10.1016/j.spasta.2022.100656

DO - 10.1016/j.spasta.2022.100656

M3 - Journal article

AN - SCOPUS:85127921508

VL - 51

SP - 1

EP - 30

JO - Spatial Statistics

JF - Spatial Statistics

SN - 2211-6753

M1 - 100656

ER -

ID: 307743477