RT Journal Article
T1 Use of correlated data for nonparametric prediction of a spatial target variable
A1 García Soidán, Maria Del Pilar Hortensia
A1 Cotos Yáñez, Tomas Raimundo
K1 1209 Estadística
K1 5401.03 Utilización de la Tierra
K1 5302 Econometría
AB 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.
PB Mathematics
SN 22277390
YR 2020
FD 2020-11-20
LK http://hdl.handle.net/11093/1870
UL http://hdl.handle.net/11093/1870
LA eng
NO Mathematics, 8(11): 2077 (2020)
NO FEDER | Ref. TEC2015–65353 – R
DS Investigo
RD 23-sep-2023