A frequency domain test for isotropy in spatial data models

Flavio Santi; Giuseppe Arbia; Marco Bee; Giuseppe Espa

doi:10.1016/j.spasta.2017.05.002

A frequency domain test for isotropy in spatial data models

Flavio Santi, Giuseppe Arbia^*, Marco Bee, Giuseppe Espa

^*Corresponding author

University of Trento

Research output: Contribution to journal › Article › peer-review

Abstract

we develop a new methodology for estimating and testing the form of anisotropiy of homogeneous spatial processes. We derive a generalized version of the isotropy test proposed by Arbvia ety al (2013) and analyse its propoerties in variius settings. Expanding on this we propose a new testing procedure in the frequency domain that allows one to estimate and test under mild conditions any form of anisotropy in homogenoues spatial processes. The power of the test is studied by means of Monte Carlo simulations performed both on regoular and irregularly spaced data. Finally the method is used to analyse soybean yelds in the US.

Original language	English
Pages (from-to)	262-278
Number of pages	17
Journal	Spatial Statistics
Volume	21
DOIs	https://doi.org/10.1016/j.spasta.2017.05.002
Publication status	Published - 2017

Keywords

spatial big data

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10.1016/j.spasta.2017.05.002

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abstract = "we develop a new methodology for estimating and testing the form of anisotropiy of homogeneous spatial processes. We derive a generalized version of the isotropy test proposed by Arbvia ety al (2013) and analyse its propoerties in variius settings. Expanding on this we propose a new testing procedure in the frequency domain that allows one to estimate and test under mild conditions any form of anisotropy in homogenoues spatial processes. The power of the test is studied by means of Monte Carlo simulations performed both on regoular and irregularly spaced data. Finally the method is used to analyse soybean yelds in the US.",

keywords = "spatial big data, spatial big data",

author = "Flavio Santi and Giuseppe Arbia and Marco Bee and Giuseppe Espa",

year = "2017",

doi = "10.1016/j.spasta.2017.05.002",

language = "English",

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pages = "262--278",

journal = "Spatial Statistics",

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TY - JOUR

T1 - A frequency domain test for isotropy in spatial data models

AU - Santi, Flavio

AU - Arbia, Giuseppe

AU - Bee, Marco

AU - Espa, Giuseppe

PY - 2017

Y1 - 2017

N2 - we develop a new methodology for estimating and testing the form of anisotropiy of homogeneous spatial processes. We derive a generalized version of the isotropy test proposed by Arbvia ety al (2013) and analyse its propoerties in variius settings. Expanding on this we propose a new testing procedure in the frequency domain that allows one to estimate and test under mild conditions any form of anisotropy in homogenoues spatial processes. The power of the test is studied by means of Monte Carlo simulations performed both on regoular and irregularly spaced data. Finally the method is used to analyse soybean yelds in the US.

AB - we develop a new methodology for estimating and testing the form of anisotropiy of homogeneous spatial processes. We derive a generalized version of the isotropy test proposed by Arbvia ety al (2013) and analyse its propoerties in variius settings. Expanding on this we propose a new testing procedure in the frequency domain that allows one to estimate and test under mild conditions any form of anisotropy in homogenoues spatial processes. The power of the test is studied by means of Monte Carlo simulations performed both on regoular and irregularly spaced data. Finally the method is used to analyse soybean yelds in the US.

KW - spatial big data

UR - http://hdl.handle.net/10807/116311

U2 - 10.1016/j.spasta.2017.05.002

DO - 10.1016/j.spasta.2017.05.002

M3 - Article

SN - 2211-6753

VL - 21

SP - 262

EP - 278

JO - Spatial Statistics

JF - Spatial Statistics

ER -

A frequency domain test for isotropy in spatial data models

Abstract

Keywords

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