Influence Functions in Semivariogram Estimation : A Comparative Study

Spatial data is analyzed in three stages of 1) estimating the variograms, 2) fitting a model for the estimated variograms and 3) predicting the value at unknown location based on the information at known locations (kriging). Recently, it has become a subject of interest to detect influential observations in these stages. Choi and Tanaka(1999) have derived influence functions in the above three stages and have proposed sensitivity analysis procedure. So far influence functions have only been derived for variograms by Gunst and Hartfield(1996). The present article makes a comparison of the performances between those influence functions for variograms derived by Choi and Tanaka(1999) and by Gunst and Hartfield(1996). A real numerical example is given to discuss the validity or usefulness of those influence functions.