1 research outputs found
Regression and Classification by Zonal Kriging
Consider a family , of
pairs of vectors and scalars that
we aim to predict for a new sample vector . Kriging models as
a sum of a deterministic function , a drift which depends on the point
, and a random function with zero mean. The zonality
hypothesis interprets as a weighted sum of random functions of a single
independent variables, each of which is a kriging, with a quadratic form for
the variograms drift. We can therefore construct an unbiased estimator
de
with minimal variance
, with the help of the
known training set points. We give the explicitly closed form for
without having calculated the inverse of the matrices.Comment: Technical Repor