Spatiotemporal MMSE Mapping

  • George Christakos
  • Dionissios T. Hristopulos
Chapter

Abstract

This chapter treats spatiotemporal minimum mean square error (MMSE) mapping techniques in considerable detail. Due to their popularity in many of scientific fields, we decided to include a separate chapter on MMSE techniques, despite the fact that in principle these techniques are special cases of the BME analysis. As we already discussed in the previous chapter, mapping techniques provide the tools for generating accurate predictive maps and for deductive analysis. Mapping requires important decisions regarding a few crucial issues, such as (i) the type of data to be included, (ii) the mapping objectives and (iii) the form of the estimator. The MMSE mapping techniques considered in this chapter are designed to incorporate mainly hard data. The other two issues are addressed as follows.

Keywords

Ozone Concentration Minimum Mean Square Error Hard Data Generalize Covariance Linear Minimum Mean Square Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • George Christakos
    • 1
  • Dionissios T. Hristopulos
    • 1
  1. 1.School of Public Health, Department of Environmental Sciences and EngineeringThe University of North CarolinaChapel HillUSA

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