Browsing by Author "Uribe Opazo, Miguel Angel"

Now showing 1 - 4 of 4
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    Influence diagnostics in elliptical spatial linear models
    (2015) De Bastiani, Fernanda; Cysneiros, Audrey Helen Mariz de Aquino; Uribe Opazo, Miguel Angel; Galea Rojas, Manuel Jesús
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    Influence diagnostics in Gaussian spatial linear models
    (TAYLOR & FRANCIS LTD, 2012) Uribe Opazo, Miguel Angel; Borssoi, Joelmir André; Galea Rojas, Manuel Jesús
    Spatial linear models have been applied in numerous fields such as agriculture, geoscience and environmen-tal 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 maybe greatly affected by the presence of atypical observations in the sampled data. The purpose of this paperis to use diagnostic techniques to assess the sensitivity of the maximum-likelihood estimators, covariancefunctions and linear predictor to small perturbations in the data and/or the spatial linear model assump-tions. The methodology is illustrated with two real data sets. The results allowed us to conclude that thepresence of atypical values in the sample data have a strong influence on thematic maps, changing the spatial dependence structure.
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    LOCAL INFLUENCE FOR SPATIAL ANALYSIS OF SOIL PHYSICAL PROPERTIES AND SOYBEAN YIELD USING STUDENT'S t-DISTRIBUTION
    (2011) Botinha Assumpcao, Rosangela Aparecida; Uribe Opazo, Miguel Angel; Galea, Manuel
    The modeling and estimation of the parameters that define the spatial dependence structure of a regionalized variable by geostatistical methods are fundamental, since these parameters, underlying the kriging of unsampled points, allow the construction of thematic maps. One or more atypical observations in the sample data can affect the estimation of these parameters. Thus, the assessment of the combined influence of these observations by the analysis of Local Influence is essential. The purpose of this paper was to propose local influence analysis methods for the regionalized variable, given that it has n-variate Student's t-distribution, and compare it with the analysis of local influence when the same regionalized variable has n-variate normal distribution. These local influence analysis methods were applied to soil physical properties and soybean yield data of an experiment carried out in a 56.68 ha commercial field in western Parana, Brazil. Results showed that influential values are efficiently determined with n-variate Student's t-distribution.
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    Slash spatial linear modeling: Soybean yield variability as a function of soil chemical properties
    (2018) Fagundes, Regiane Slongo; Uribe Opazo, Miguel Angel; Guedes, Luciana Pagliosa Carvalho; Galea Rojas, Manuel Jesús