ABSTRACT
Spartan Spatial Random Fields (SSRF’s) provide a new class of models for spatial dependence. Their main advantages compared to classical geostatistical techniques are computational efficiency and parametric parsimony. This paper focuses on the identification of the SSRF parameters from data contaminated with uncorrelated random noise, known by the name ‘nugget effect’ in geostatistical applications.
