ISSN:
1573-8868
Keywords:
Bayesian inference
;
elicited prior
;
hierarchical models
;
honesty parameter
;
kriging
;
optimal sequential sample design
Source:
Springer Online Journal Archives 1860-2000
Topics:
Geosciences
,
Mathematics
Notes:
Abstract Spatial data are often sparse by nature. However, in many instances, information may exist in the form of “soft” data, such as expert opinion. Scientists in the field often have a good understanding of the phenomenon under study and may be able to provide valuable information on its likely behavior. It is thus useful to have a sensible mechanism that incorporates expert opinion in inference. The Bayesian paradigm suffers from an inherent subjectivity that is unacceptable to many scientists. Aside from this philosophical problem, elicitation of prior distributions is a difficult task. Moreover, an intentionally misleading expert can have substantial influence on Bayesian inference. In our experience, eliciting data is much more natural to the experts than eliciting prior distributions on the parameters of a probability model that is a purely statistical construct. In this paper we elicit data, i.e., guess values for the realization of the process, from the experts. Utilizing a hierarchical modeling framework, we combine elicited data and actual observed data for inferential purposes. A distinguishing feature of this approach is that even an intentionally misleading expert proves to be useful. Theoretical results and simulations illustrate that incorporating expert opinion via elicited data substantially improves the estimation, prediction, and design aspects of statistical inference for spatial data.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1023/A:1007525900030
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