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Table 5 Performance comparison of geostatistical and Bayesian estimators: goodness and precision of models of uncertainty.

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

Estimators Lung cancer Cervix cancer
GOODNESS STATISTIC Average % best result Average % best result
BYM model 0.949 48 0.950 48
Point Poisson kriging (adjacent counties) 0.941 28 0.951 24
ATA Poisson kriging (adjacent counties) 0.922 14 0.939 16
ATA Poisson kriging (32 neighbors) 0.914 10 0.927 12
AVERAGE WIDTH OF PI     
BYM model 2.544 14 0.816 4
Point Poisson kriging (adjacent counties) 2.439 20 0.813 0
ATA Poisson kriging (adjacent counties) 2.348 12 0.745 0
ATA Poisson kriging (32 neighbors) 2.313 54 0.691 96
% ACCURATE AND PRECISE PI     
BYM model vs Point PK (adjacent counties) 9.35 6 12.73 8
BYM model vs ATA PK (adjacent counties) 7.53 4 6.22 2
BYM model vs ATA PK (32 neighbors) 7.35 4 4.30 4
Point PK (adjacent counties) vs BYM model 34.87 36 28.02 32
ATA PK (adjacent counties) vs BYM model 28.12 24 36.02 20
ATA PK (32 neighbors) vs BYM model 27.66 26 40.28 34
  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 last statistic, based on the pair wise comparison of methods, reports the percentage of probability intervals (PI) that are jointly more accurate and precise. Bold numbers refer to best performances: goodness close to one, narrow probability intervals, and high percentage of accurate and precise PIs. The second column gives the percentage of realizations where the particular method yields the best results.