Inference for spatial regression models with functional response using a permutational approach
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F22%3A73614892" target="_blank" >RIV/61989592:15310/22:73614892 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0047259X21001718" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0047259X21001718</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.jmva.2021.104893" target="_blank" >10.1016/j.jmva.2021.104893</a>
Alternative languages
Result language
angličtina
Original language name
Inference for spatial regression models with functional response using a permutational approach
Original language description
The aim of this work is to introduce an approach to null hypothesis significance testing in a functional linear model for spatial data. The proposed method is capable of dealing with the spatial structure of data by building a permutation testing procedure on spatially filtered residuals of a spatial regression model. Indeed, due to the spatial dependence existing among the data, the residuals of the regression model are not exchangeable, breaking the basic assumptions of the Freedman and Lane permutation scheme. Instead, it is proposed here to estimate the variance-covariance structure of the residuals by variography, remove this correlation by spatial filtering residuals and base the permutation test on these approximately exchangeable residuals. A simulation study is conducted to evaluate the performance of the proposed method in terms of empirical size and power, examining its behavior under different covariance settings. We show that neglecting the residuals spatial structure in the permutation scheme (thus permuting the correlated residuals directly) yields a very liberal testing procedures, whereas the proposed procedure is close to the nominal size of the test. The methodology is demonstrated on a real world data set on the amount of waste production in the Venice province of Italy.
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
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
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 MULTIVARIATE ANALYSIS
ISSN
0047-259X
e-ISSN
1095-7243
Volume of the periodical
189
Issue of the periodical within the volume
MAY
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
"104893-1"-"104893-12"
UT code for WoS article
000759636000007
EID of the result in the Scopus database
2-s2.0-85119011008