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Contributions to Sampling Statistics pp 151–165Cite as

Sampling Theory and Geostatistics: A Way of Reconciliation

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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 model-assisted 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.

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Author information

Authors and Affiliations

  1. University of Perugia, Perugia, Italy

    Giorgio E. Montanari & Giuseppe Cicchitelli

Authors
  1. Giorgio E. Montanari
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  2. Giuseppe Cicchitelli
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Corresponding author

Correspondence to Giorgio E. Montanari .

Editor information

Editors and Affiliations

  1. Dept. of Sociology and Social Research, University of Milano-Bicocca, Milano, Milano, Italy

    Fulvia Mecatti

  2. Dept. of Statistical Sciences, Sapienza - University of Rome, Roma, Roma, Italy

    Pier Luigi Conti

  3. Dept. Economics, Finance and Statistics, University of Perugia, Perugia, Perugia, Italy

    Maria Giovanna Ranalli

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© 2014 Springer International Publishing Switzerland

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Montanari, G.E., Cicchitelli, G. (2014). Sampling Theory and Geostatistics: A Way of Reconciliation. In: Mecatti, F., Conti, P., Ranalli, M. (eds) Contributions to Sampling Statistics. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-05320-2_10

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