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Table 6 Performance comparison of geostatistical and Bayesian estimators: discriminatory power of models of uncertainty.

From: How does Poisson kriging compare to the popular BYM model for mapping disease risks?

Estimators Lung cancer Cervix cancer
LOW RISK THRESHOLD (RR = 1) Average % best result Average % best result
BYM model 4.009 14 3.532 16
Point Poisson kriging (adjacent counties) 4.112 20 3.751 4
ATA Poisson kriging (adjacent counties) 4.186 36 3.915 38
ATA Poisson kriging (32 neighbors) 4.132 30 3.995 42
HIGH RISK THRESHOLD RR = 1.1   RR = 1.25  
BYM model 7.037 6 5.859 2
Point Poisson kriging (adjacent counties) 7.842 12 6.300 0
ATA Poisson kriging (adjacent counties) 8.059 36 6.721 6
ATA Poisson kriging (32 neighbors) 8.243 46 7.381 92
  1. Results obtained on average over 50 realizations generated for Regions 1 and 2. Poisson kriging was conducted using either adjacent counties (same neighbors as BYM Model) or the 32 closest counties in terms of distance between population-weighted centroids. ATA kriging accounts for the shape and size of the counties in the analysis. The reported statistic is the average probability of exceeding a risk threshold for counties with true risk above that threshold divided by the average probability for the remaining counties. A low (Relative Risk, RR = 1) and high risk threshold (RR = 1.1, RR = 1.25) were considered. Bold numbers refer to best performances: large probability ratio. The second column gives the percentage of realizations where the particular method yields the best results.