In a previous paper, the authors addressed the problem of the estimation of the mean of a spatial population in a design-based context and proposed a modelassisted estimator based on penalized regression splines. This paper goes a step further exploring the performance of the leave-one-out cross-validation criterion for selecting the amount of smoothing in the regression spline assisting model and for estimating the variance of the proposed estimator. The attention is focused on continuous spatial populations. A simulation study provides an empirical efficiency comparison between this estimator and the kriging predictor by means of Monte Carlo experiments.

Sampling theory and Geostatistics: a way of reconciliation

MONTANARI, Giorgio Eduardo;CICCHITELLI, Giuseppe
2014

Abstract

In a previous paper, the authors addressed the problem of the estimation of the mean of a spatial population in a design-based context and proposed a modelassisted estimator based on penalized regression splines. This paper goes a step further exploring the performance of the leave-one-out cross-validation criterion for selecting the amount of smoothing in the regression spline assisting model and for estimating the variance of the proposed estimator. The attention is focused on continuous spatial populations. A simulation study provides an empirical efficiency comparison between this estimator and the kriging predictor by means of Monte Carlo experiments.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/1175477
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact