Local influence when fitting Gaussian spatial linear models: an agriculture application

D.M. Grzegozewski, M.A. Uribe-Opazo, F. De Bastiani, and M. Galea. 2013. Local influence when fitting Gaussian spatial linear models: an agriculture application. Cien. Inv. Agr. 40(3): 523-535. Outliers can adversely affect how data fit into a model. Obviously, an analysis of dependent data is diffe...

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Autores principales:	Grzegozewski,Denise M, Uribe-Opaz,Miguel A, De Bastiani,Fernanda, Galea,Manuel
Lenguaje:	English
Publicado:	Pontificia Universidad Católica de Chile. Facultad de Agronomía e Ingeniería Forestal 2013
Materias:	Geostatistical influence diagnostics maximum likelihood outliers spatial variability
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-16202013000300006
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spelling	oai:scielo:S0718-162020130003000062014-09-08Local influence when fitting Gaussian spatial linear models: an agriculture applicationGrzegozewski,Denise MUribe-Opaz,Miguel ADe Bastiani,FernandaGalea,Manuel Geostatistical influence diagnostics maximum likelihood outliers spatial variability D.M. Grzegozewski, M.A. Uribe-Opazo, F. De Bastiani, and M. Galea. 2013. Local influence when fitting Gaussian spatial linear models: an agriculture application. Cien. Inv. Agr. 40(3): 523-535. Outliers can adversely affect how data fit into a model. Obviously, an analysis of dependent data is different from that of independent data. In the latter, i.e., in cases involving spatial data, local outliers can differ from the data in the neighborhood. In this article, we used the local influence technique to identify influential points in the response variables using two different schemes of perturbations. We applied this technique to soil chemical properties and soybean yield. We evaluated the effects of the influential points on the spatial model selection, the parameter estimation by maximum likelihood and the construction of thematic maps by kriging. In the construction of the thematic maps in studies with and without the influential points, there were changes in the levels of nutrients, allowing for the appropriate application of input, generating greater savings for the producer and contributing to the protection of the environment.info:eu-repo/semantics/openAccessPontificia Universidad Católica de Chile. Facultad de Agronomía e Ingeniería ForestalCiencia e investigación agraria v.40 n.3 20132013-12-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-16202013000300006en10.4067/S0718-16202013000300006
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Geostatistical influence diagnostics maximum likelihood outliers spatial variability
spellingShingle	Geostatistical influence diagnostics maximum likelihood outliers spatial variability Grzegozewski,Denise M Uribe-Opaz,Miguel A De Bastiani,Fernanda Galea,Manuel Local influence when fitting Gaussian spatial linear models: an agriculture application
description	D.M. Grzegozewski, M.A. Uribe-Opazo, F. De Bastiani, and M. Galea. 2013. Local influence when fitting Gaussian spatial linear models: an agriculture application. Cien. Inv. Agr. 40(3): 523-535. Outliers can adversely affect how data fit into a model. Obviously, an analysis of dependent data is different from that of independent data. In the latter, i.e., in cases involving spatial data, local outliers can differ from the data in the neighborhood. In this article, we used the local influence technique to identify influential points in the response variables using two different schemes of perturbations. We applied this technique to soil chemical properties and soybean yield. We evaluated the effects of the influential points on the spatial model selection, the parameter estimation by maximum likelihood and the construction of thematic maps by kriging. In the construction of the thematic maps in studies with and without the influential points, there were changes in the levels of nutrients, allowing for the appropriate application of input, generating greater savings for the producer and contributing to the protection of the environment.
author	Grzegozewski,Denise M Uribe-Opaz,Miguel A De Bastiani,Fernanda Galea,Manuel
author_facet	Grzegozewski,Denise M Uribe-Opaz,Miguel A De Bastiani,Fernanda Galea,Manuel
author_sort	Grzegozewski,Denise M
title	Local influence when fitting Gaussian spatial linear models: an agriculture application
title_short	Local influence when fitting Gaussian spatial linear models: an agriculture application
title_full	Local influence when fitting Gaussian spatial linear models: an agriculture application
title_fullStr	Local influence when fitting Gaussian spatial linear models: an agriculture application
title_full_unstemmed	Local influence when fitting Gaussian spatial linear models: an agriculture application
title_sort	local influence when fitting gaussian spatial linear models: an agriculture application
publisher	Pontificia Universidad Católica de Chile. Facultad de Agronomía e Ingeniería Forestal
publishDate	2013
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-16202013000300006
work_keys_str_mv	AT grzegozewskidenisem localinfluencewhenfittinggaussianspatiallinearmodelsanagricultureapplication AT uribeopazmiguela localinfluencewhenfittinggaussianspatiallinearmodelsanagricultureapplication AT debastianifernanda localinfluencewhenfittinggaussianspatiallinearmodelsanagricultureapplication AT galeamanuel localinfluencewhenfittinggaussianspatiallinearmodelsanagricultureapplication
_version_	1714202151949959168

Local influence when fitting Gaussian spatial linear models: an agriculture application

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