Nonparametric Testing of the Covariate Significance for Spatial Point Patterns under the Presence of Nuisance Covariates
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F24%3A10488783" target="_blank" >RIV/00216208:11320/24:10488783 - isvavai.cz</a>
Alternative codes found
RIV/60076658:12220/24:43908278
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=ZZTb9k21f6" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=ZZTb9k21f6</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/10618600.2024.2357626" target="_blank" >10.1080/10618600.2024.2357626</a>
Alternative languages
Result language
angličtina
Original language name
Nonparametric Testing of the Covariate Significance for Spatial Point Patterns under the Presence of Nuisance Covariates
Original language description
Determining the relevant spatial covariates is one of the most important problems in the analysis of point patterns. Parametric methods may lead to incorrect conclusions, especially when the model of interactions between points is wrong. Therefore, we propose a fully nonparametric approach to testing significance of a covariate, taking into account the possible effects of nuisance covariates. Our tests match the nominal significance level, and their powers are comparable with the powers of parametric tests in cases where both the model for intensity function and the model for interactions are correct. When the parametric model for the intensity function is wrong, our tests achieve higher powers. The proposed methods rely on Monte Carlo testing and take advantage of the newly introduced concepts: the covariate-weighted residual measure and nonparametric residuals. We also define a correlation coefficient between a point process and a covariate and a partial correlation coefficient quantifying the dependence between a point process and a covariate of interest while removing the influence of nuisance covariates. Supplementary materials for this article are available online.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Computational and Graphical Statistics
ISSN
1061-8600
e-ISSN
1537-2715
Volume of the periodical
33
Issue of the periodical within the volume
4
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
1434-1445
UT code for WoS article
001253705100001
EID of the result in the Scopus database
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