Capítulos

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Examinando Capítulos por Departamento "Estadística, Investigación Operativa y Cálculo Numérico"

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    Robust morphometric analysis based on landmarks
    (Springer, 2018-03-01) García Pérez, Alfonso
    Procrustes Analysis is a Morphometric method based on Configurations of Landmarks that estimates the superimposition parameters by least-squares; for this reason, the procedure is very sensitive to outliers. There are classical results, based on the normality of the observations, to test whether there are significant differences between individuals. In this paper we determine a Von Mises plus Saddlepoint approximation for the tail probability (p-value) of this test for the Procrustes Statistic, when the observations come from a model close to the normal.
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    Trimmed spatio-temporal variogram estimator
    (Springer, 2022-08-25) García Pérez, Alfonso
    The spatio-temporal variogram is the key element in spatiotemporal prediction based on kriging, but the classical estimator of this parameter is very sensitive to outliers. In this contributed paper we propose a trimmed estimator of the spatio-temporal variogram as a robust estimator. We obtain an accurate approximation of its distribution with small samples sizes and a scale contaminated normal model.We conclude with an example with real data.
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    Variogram model selection
    (Springer, 2022-06-28) García Pérez, Alfonso
    A common problem in geostatistics is variogram estimation, in order to choose an acceptable model for kriging. Nevertheless, there is no standard method, first, to test if a particular model can be accepted as valid and, second, to choose among several competing variogram models. The problem is even more complex if, in addition, there are outliers in the data. In this paper we propose to use the distribution of some classical and robust variogram estimators to test, first, the validity of a particular model, accepting it if the p-value of the test, with this particular model as null hypothesis, is large enough and, second, to compare several competing models, choosing the model with the largest p-value among several acceptable models.