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    A Universal Kriging predictor for spatially dependent functional data of a Hilbert Space

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    We address the problem of predicting spatially dependent functional data belonging to a Hilbert space, with a Functional Data Analysis approach. Having defined new global measures of spatial variability for functional random processes, we derive a Universal Kriging predictor for functional data. Consistently with the new established theoretical results, we develop a two-step procedure for predicting georeferenced functional data: first model selection and estimation of the spatial mean (drift), then Universal Kriging prediction on the basis of the identified model. The proposed methodology is applied to daily mean temperatures curves recorded in the Maritimes Provinces of Canada

    Prediction of non-stationary functional data: Universal Kriging in a Hilbert space

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    In an increasing number of studies, collected data are curves; when functional data are spatially dependent, the problem of prediction assumes a key role. In this work we deal with spatially distributed functional data proposing an extension of some geostatistical tools to non-stationary functional random fields, with a Functional Data Analysis approach. An extension of the Universal Kriging method to elements of a Hilbert space is proposed, in a coherent frame of definitions and assumptions. Consistently with these new theoretical results, a method for prediction of non-stationary spatial dependent functional data is proposed and then developed in three steps: model selection for the drift term, decomposition of the original process into a deterministic term (the drift) and a residual stochastic process, Universal Kriging prediction. The proposed procedure is applied to daily mean temperatures curves observed in 35 meteorological stations located in Canada's Maritimes Provinces

    Monitoraggio per via gascromatografica dello sviluppo fungino in prootti carnei

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    Fungal growth of inoculated samples of cured ham during storage at different temperatures was monitored by means the ethanol developing into the samples' headspace vapor. Showing the growth of the inoculated mould (Penicillium expansum) the most detectable alteration, the limit of acceptability of the samples of cured ham was evaluated choosing an arbitrary threshold of the ethanol developed
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