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.
2014
47th SIS Scientific Meeting of the Italian Statistica Society
Cagliari
June 2014
1
6
Ignaccolo, Rosaria; FRANCO VILLORIA, Maria
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).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1198045
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