Speaker
Description
In this talk, the problem of selecting a set of design points for universal kriging,
which is a widely used technique for spatial data analysis, is further
investigated. We are interested in optimal designs for prediction and present
a new design criterion that aims at simultaneously minimizing the variation
of the prediction errors at various points. This optimality criterion is based
on the generalized variance (GV) and selects the design points in order to
make simultaneous predictions of the random variable of interest at a finite
number of unsampled locations with maximum precision. Specifically, a
correlated random field given by a linear model with an unknown parameter
vector and a spatial error correlation structure is considered as response.
Though the proposed design is effective and there are efficient techniques
for incrementally building designs for that criterion the method is limited to
simultaneous predictions at a finite number of locations. We are convinced
that this restriction can be lifted and the method may be generalized to minimizing
the generalized prediction variance over the design space. Currently
we have not yet solved the problem which addresses infinite determinants
but we may present interesting and promising preliminary results.
| Type of presentation | Talk |
|---|---|
| Classification | Mainly methodology |
| Keywords | optimal design for simultaneous prediction |