Comparison of coIK, IK and mIK Performances for Modeling Conditional Probabilities of Categorical Variables

Abstract

A performance comparison of three algorithms (coindicator kriging, multiple indicator kriging, median indicator kriging) for estimating conditional probabilities of categorical variables is presented. The reference soil data set includes 2649 locations at which the soil type was determined. Three subsets of 50, 100 and 500 sample locations were randomly selected and used to estimate the probability for the different soil types at the remaining locations. In all cases the variograms required were modeled using the complete data set. The comparison of estimated vs true probabilities shows that, whatever the number of conditioning data, the theoretically better coIK does not provide more accurate re-estimates than the two other algorithms. The latter two algorithms involve less variogram modeling effort and smaller computational cost. Furthermore, the number of order relation deviations is showed to be significantly higher for the coIK results.