11. Adaptive Sampling Design for Spatio-Temporal Prediction

  1. Jorge Mateu3 and
  2. Werner G. Müller4
  1. Thomas R. Fanshawe1 and
  2. Peter J. Diggle1,2

Published Online: 11 OCT 2012

DOI: 10.1002/9781118441862.ch11

Spatio-Temporal Design: Advances in Efficient Data Acquisition

Spatio-Temporal Design: Advances in Efficient Data Acquisition

How to Cite

Fanshawe, T. R. and Diggle, P. J. (2012) Adaptive Sampling Design for Spatio-Temporal Prediction, in Spatio-Temporal Design: Advances in Efficient Data Acquisition (eds J. Mateu and W. G. Müller), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9781118441862.ch11

Editor Information

  1. 3

    Department of Mathematics, University of Jaume I of Castellon, Spain

  2. 4

    Department of Applied Statistics, Johannes Kepler University Linz, Austria

Author Information

  1. 1

    Lancaster Medical School, Lancaster University, UK

  2. 2

    Institute of Infection and Global Health, University of Liverpool, UK

Publication History

  1. Published Online: 11 OCT 2012
  2. Published Print: 16 NOV 2012

ISBN Information

Print ISBN: 9780470974292

Online ISBN: 9781118441862

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Keywords:

  • Gaussian model;
  • model-based design;
  • spatio-temporal adaptive design;
  • stochastic process;
  • Upper Austria

Summary

Environmental monitoring provides a typical setting that gives rise to spatio-temporal design problems. This chapter considers the model-based design, in which the optimal design problem requires two key features to be specified: (i) a statistical or mathematical model for the process under consideration; and, (ii) a criterion with respect to which the design is required to be optimized. After reviewing spatial and spatio-temporal adaptive designs it considers the performance of adaptive design-finding algorithms with respect to these for two different models for stochastic process S: the stationary Gaussian model; and a dynamic process convolution model. The chapter uses the second of these models to consider adaptive designs for the Upper Austria rainfall data. It concludes that adaptive designs should be constructed by a criterion that directly measures the extent to which the primary scientific goal of the analysis is being met, and should therefore be strongly context-dependent.

Controlled Vocabulary Terms

Gaussian process; stochastic processes