Adaptive Sampling Design for Spatio-Temporal Prediction

Thomas R. Fanshawe, Peter J. Diggle

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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.

Original languageEnglish (US)
Title of host publicationSpatio-temporal Design: Advances in Efficient Data Acquisition
Publisherwiley
Pages249-268
Number of pages20
ISBN (Print)9781118441862, 9780470974292
DOIs
StatePublished - Oct 11 2012
Externally publishedYes

Keywords

  • Gaussian model
  • Model-based design
  • Spatio-temporal adaptive design
  • Stochastic process
  • Upper austria

ASJC Scopus subject areas

  • Mathematics(all)

Fingerprint Dive into the research topics of 'Adaptive Sampling Design for Spatio-Temporal Prediction'. Together they form a unique fingerprint.

Cite this