Geostatistics provides an efficient tool for mapping environmental variables from observations and layers of explanatory variables. The number and configuration of the observations importantly determine the accuracy of geostatistical inference and prediction. Data collection is costly, and coarse sampling may lead to large uncertainties in interpolated maps. In such case, additional information may be gathered from experts who are knowledgeable about the spatial variability of environmental variables. Statistical expert elicitation has gradually become a mature research field and has proved to be able to extract from experts reliable information to form a sound scientific database. In this thesis, expert knowledge has been elicited and incorporated in geostatistical models for inference and prediction. Various extensions to the expert elicitation literature were required to make it suitable for elicitation of spatial data. The use of expert knowledge in geostatistical research is promising, yet challenging.
|Qualification||Doctor of Philosophy|
|Award date||30 Jun 2014|
|Place of Publication||Wageningen|
|Publication status||Published - 2014|
- spatial statistics
- statistical inference
- bayesian theory