Abstract
The spatio-temporal variogram is the key element in spatio-temporal 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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The author is very grateful to the referee and to the Ministerio de Ciencia e Innovación.
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García-Pérez, A. (2023). Trimmed Spatio-Temporal Variogram Estimator. In: García-Escudero, L.A., et al. Building Bridges between Soft and Statistical Methodologies for Data Science . SMPS 2022. Advances in Intelligent Systems and Computing, vol 1433. Springer, Cham. https://doi.org/10.1007/978-3-031-15509-3_23
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DOI: https://doi.org/10.1007/978-3-031-15509-3_23
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