Influence diagnostics in Gaussian spatial linear models
Spatial linear models have been applied in numerous fields such as agriculture, geoscience and environmental sciences, among many others. Spatial dependence structure modelling, using a geostatistical approach, is an indispensable tool to estimate the parameters that define this structure.
However, this estimation may be greatly affected by the presence of atypical observations in the sampled data. The purpose of this paper is to use diagnostic techniques to assess the sensitivity of the maximum-likelihood estimators, covariance functions and linear predictor to small perturbations
in the data and/or the spatial linear model assumptions. The methodology is illustrated with two real data sets. The results allowed us to conclude that the presence of atypical values in the sample data have a strong influence on thematic maps, changing the spatial dependence structure.
Keywords: Gaussian models; influence diagnostics and precision agriculture; spatial statistics
Document Type: Research Article
Affiliations: 1: Centro de Ciências Exatas e Tecnológicas,Universidade Estadual do Oeste do Paraná, Brazil 2: Instituto de Matemática e Estatística,Universidade de São Paulo, Brazil 3: Departamento de Estadística,Pontificia Universidad Católica de Chile, Chile
Publication date: 01 March 2012
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