Técnicas Para Detecção De Pontos Influentes Em Variáveis Contínuas Regionalizadas

Richetti, Jonathan; Uribe-Opazo, Miguel Ángel; Bastiani, Fernanda De; Johann, Jerry Adriani

doi:10.1590/1809-4430-eng.agric.v36n1p152-165/2016

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Therefore, it is important to evaluate its sensitivity through influence diagnostics, which may be carried out by different evaluation methods, one of them being local influence assessment. The aim of this assessment is to evaluate the goodness of fit of the model, and the robustness of its estimates when small perturbations are introduced in the model and/or in the dataset (Cook, 1986;Zhu and Lee, 2001;Jonathan et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Slash Spatial Linear Modeling: Soybean Yield Variability as a Function of Soil Chemical Properties

Fagundes

Uribe-Opazo

Guedes

et al. 2018

Rev. Bras. Ciênc. Solo

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In geostatistical modeling of soil chemical properties, one or more influential observations in a dataset may impair the construction of interpolation maps and their accuracy. An alternative to avoid the problem would be to use most robust models, based on distributions that have heavier tails. Therefore, this study proposes a spatial linear model based on the slash distribution (SSLM) in order to characterize the spatial variability of soybean yields as a function of soil chemical properties. The likelihood ratio statistic (LR) was applied to verify the significance of parameters associated with the model. We evaluated the sensitivity of the maximum likelihood estimators by means of local influence analysis for both the soybean response and the linear predictor. In the proposed model, we analyzed data gathered from a commercial grain production area (127.18 ha) located in the western part of the state of Paraná (Brazil). The results showed that the slash distribution allowed us to adjust the high kurtosis of the data set distribution and the LR test confirmed that the soil chemical properties of phosphorus, potassium, pH, and organic matter were significant for the SSLM. Diagnostic analysis indicated that the atypical value of the sample set was not influential in the parameter estimation process. Construction of the interpolation map based on the proposed model is not affected when considering the atypical and/or influential observations. Thus, SSLM becomes a robust alternative in the study of soybean yield variability as a function of soil chemical properties, making it possible to investigate the productive potential of the areas.

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Section: Introductionmentioning

confidence: 99%

Slash Spatial Linear Modeling: Soybean Yield Variability as a Function of Soil Chemical Properties

Fagundes

Uribe-Opazo

Guedes

et al. 2018

Rev. Bras. Ciênc. Solo

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Directional Differences in Thematic Maps of Soil Chemical Attributes with Geometric Anisotropy

Ribeiro,

Maltauro,

Guedes

et al. 2024

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In the study of the spatial variability of soil chemical attributes, the process is considered anisotropic when the spatial dependence structure differs in relation to the direction. Anisotropy is a characteristic that influences the accuracy of the thematic maps that represent the spatial variability of the phenomenon. Therefore, the linear anisotropic Gaussian spatial model is important for spatial data that present anisotropy, and incorporating this as an intrinsic characteristic of the process that describes the spatial dependence structure improves the accuracy of the spatial estimation of the values of a georeferenced variable in unsampled locations. This work aimed at quantifying the directional differences existing in the thematic map of georeferenced variables when incorporating or not incorporating anisotropy into the spatial dependence structure through directional spatial autocorrelation. For simulated data and soil chemical properties (carbon, calcium and potassium), the Moran directional index was calculated, considering the predicted values at unsampled locations, and taking into account estimated isotropic and anisotropic geostatistical models. The directional spatial autocorrelation was effective in evidencing the directional difference between thematic maps elaborated with estimated isotropic and anisotropic geostatistical models. This measure evidenced the existence of an elliptical format of the subregions presented by thematic maps in the direction of anisotropy that indicated a greater spatial continuity for greater distances between pairs of points.

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Técnicas Para Detecção De Pontos Influentes Em Variáveis Contínuas Regionalizadas

Cited by 2 publications

References 19 publications

Slash Spatial Linear Modeling: Soybean Yield Variability as a Function of Soil Chemical Properties

Slash Spatial Linear Modeling: Soybean Yield Variability as a Function of Soil Chemical Properties

Directional Differences in Thematic Maps of Soil Chemical Attributes with Geometric Anisotropy

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