Inference for spatial regression models with functional response using a permutational approach

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Inference for spatial regression models with functional response using a permutational approach

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Inference for spatial regression models with functional response using a permutational approach

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

JOURNAL OF MULTIVARIATE ANALYSIS

ISSN

0047-259X

e-ISSN

1095-7243

Svazek periodika

189

Číslo periodika v rámci svazku

MAY

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

"104893-1"-"104893-12"

Kód UT WoS článku

000759636000007

EID výsledku v databázi Scopus

2-s2.0-85119011008