Use of Correlated Data for Nonparametric Prediction of a Spatial Target Variable
| Autor: | Tomás R. Cotos-Yáñez, Pilar García-Soidán |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Spatial variable
bandwidth parameter Multivariate statistics 010504 meteorology & atmospheric sciences Computer science General Mathematics Mean squared prediction error 01 natural sciences 010104 statistics & probability covariogram Kriging Statistics 5302 Econometría Computer Science (miscellaneous) 0101 mathematics Engineering (miscellaneous) Spatial analysis 0105 earth and related environmental sciences cokriging 1209 Estadística lcsh:Mathematics Bandwidth (signal processing) Nonparametric statistics prediction lcsh:QA1-939 5401.03 Utilización de la Tierra kernel method Kernel method |
| Zdroj: | Mathematics, Vol 8, Iss 2077, p 2077 (2020) Investigo. Repositorio Institucional de la Universidade de Vigo Universidade de Vigo (UVigo) Mathematics Volume 8 Issue 11 |
| ISSN: | 2227-7390 |
| Popis: | The kriging methodology can be applied to predict the value of a spatial variable at an unsampled location, from the available spatial data. Furthermore, additional information from secondary variables, correlated with the target one, can be included in the resulting predictor by using the cokriging techniques. The latter procedures require a previous specification of the multivariate dependence structure, difficult to characterize in practice in an appropriate way. To simplify this task, the current work introduces a nonparametric kernel approach for prediction, which satisfies good properties, such as asymptotic unbiasedness or the convergence to zero of the mean squared prediction error. The selection of the bandwidth parameters involved is also addressed, as well as the estimation of the remaining unknown terms in the kernel predictor. The performance of the new methodology is illustrated through numerical studies with simulated data, carried out in different scenarios. In addition, the proposed nonparametric approach is applied to predict the concentrations of a pollutant that represents a risk to human health, the cadmium, in the floodplain of the Meuse river (Netherlands), by incorporating the lead level as an auxiliary variable. FEDER | Ref. TEC2015–65353 – R España. Ministerio de Ciencia e Innovación | Ref. MTM2017–89422 – P Xunta de Galicia ( | Ref. GRC ED431C 2016/040 |
| Databáze: | OpenAIRE |
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