We predict a curve at an unmonitored site taking into account exogenous variables using a functional kriging model with external drift and, alternatively, an additive model with a spatio-temporal smooth term. To evaluate uncertainty of the predicted curves, a semi-parametric bootstrap approach is used for the first, while standard inference is used for the second. The performance of both approaches is illustrated on pollutant functional data.
Kriging for functional data: uncertainty assessment / Ignaccolo, Rosaria; FRANCO VILLORIA, Maria. - (2014), pp. 1-6. (Intervento presentato al convegno 47th SIS Scientific Meeting of the Italian Statistica Society tenutosi a Cagliari nel June 2014).
Kriging for functional data: uncertainty assessment
Maria Franco Villoria
2014
Abstract
We predict a curve at an unmonitored site taking into account exogenous variables using a functional kriging model with external drift and, alternatively, an additive model with a spatio-temporal smooth term. To evaluate uncertainty of the predicted curves, a semi-parametric bootstrap approach is used for the first, while standard inference is used for the second. The performance of both approaches is illustrated on pollutant functional data.File | Dimensione | Formato | |
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2014_SIS_fked_2953_4aperto_1343014.pdf
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2014_SIS_fked_2953_4aperto_1343014.pdf
Accesso riservato
Dimensione
2.25 MB
Formato
Adobe PDF
|
2.25 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
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