A universal kriging approach for spatial functional data

By William Caballero, Ramón Giraldo, Jorge Mateu. Stochastic environmental research and risk assessment (Print), 2013

93 citations recorded by the source corpus.

Subfields: Environmental Chemistry, Environmental Engineering, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/aafb6416b5e5bc43b5ac50ce54612bb058231f03

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Chemistry

1. X. Lin
2. Matilde Dalla Rosa
3. Guojun Gan
4. Alessandra Menafoglio
5. Piercesare Secchi

In Environmental Engineering

1. Laura M. Sangalli
2. James O. Ramsay
3. G. Mazza
4. X. Lin
5. Matilde Dalla Rosa
6. Guojun Gan
7. Alessandra Menafoglio
8. Piercesare Secchi

In Nature and Landscape Conservation

1. Laura M. Sangalli
2. X. Lin
3. Matilde Dalla Rosa
4. Guojun Gan
5. Alessandra Menafoglio
6. Piercesare Secchi

Citations in the corpus

• A Universal Kriging predictor for spatially dependent functional data of a Hilbert Space (2013, methodology): “To this end, two techniques for kriging non-stationary functional data belonging to L 2 have been developed concurrently with the present work, proposing a Residual Kriging approach (Reyes et al., 2012) and a Universal Kriging approach (Caballero et al., 2013).”
• A penalized regression model for spatial functional data with application to the analysis of the production of waste in Venice province (2016, background): “Recent techniques developing universal kriging approaches for spatially dependent functional data are offered by Caballero et al. (2013), Menafoglio et al. (2013) and Menafoglio et al. (2014).”
• Valuation of large variable annuity portfolios under nested simulation: A functional data approach (2015, methodology): “Other relevant works on portfolio valuation include Chen et al. (2012), Broadie et al.”