Header

UZH-Logo

Maintenance Infos

Monte Carlo permutation tests for assessing spatial dependence at different scales


Wang, Craig; Furrer, Reinhard (2020). Monte Carlo permutation tests for assessing spatial dependence at different scales. In: La Rocca, Michele; Brunero, Liseo; Salmaso, Luigi. Nonparametric Statistics. ISNPS 2018. Cham, Switzerland, 503-511.

Abstract

Spatially dependent residuals arise as a result of missing or misspecified spatial variables in a model. Such dependence is observed in different areas, including environmental, epidemiological, social and economic studies. It is crucial to take the dependence into modelling consideration to avoid spurious associations between variables of interest or to avoid wrong inferential conclusions due to underestimated uncertainties. An insight about the scales at which spatial dependence exist can help to comprehend the underlying physical process and to select suitable spatial interpolation methods. In this paper, we propose two Monte Carlo permutation tests to (1) assess the existence of overall spatial dependence and (2) assess spatial dependence at small scales, respectively. A p-value combination method is used to improve statistical power of the tests. We conduct a simulation study to reveal the advantages of our proposed methods in terms of type I error rate and statistical power. The tests are implemented in an open-source R package variosig.

Abstract

Spatially dependent residuals arise as a result of missing or misspecified spatial variables in a model. Such dependence is observed in different areas, including environmental, epidemiological, social and economic studies. It is crucial to take the dependence into modelling consideration to avoid spurious associations between variables of interest or to avoid wrong inferential conclusions due to underestimated uncertainties. An insight about the scales at which spatial dependence exist can help to comprehend the underlying physical process and to select suitable spatial interpolation methods. In this paper, we propose two Monte Carlo permutation tests to (1) assess the existence of overall spatial dependence and (2) assess spatial dependence at small scales, respectively. A p-value combination method is used to improve statistical power of the tests. We conduct a simulation study to reveal the advantages of our proposed methods in terms of type I error rate and statistical power. The tests are implemented in an open-source R package variosig.

Statistics

Citations

Dimensions.ai Metrics

Altmetrics

Additional indexing

Item Type:Book Section, not_refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Mathematics
07 Faculty of Science > Institute for Computational Science
Dewey Decimal Classification:340 Law
610 Medicine & health
510 Mathematics
Scopus Subject Areas:Physical Sciences > General Mathematics
Language:English
Date:1 January 2020
Deposited On:17 Dec 2020 15:10
Last Modified:31 Jan 2021 14:04
ISBN:9783030573058
OA Status:Closed
Publisher DOI:https://doi.org/10.1007/978-3-030-57306-5_45

Download

Full text not available from this repository.
View at publisher

Get full-text in a library