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Table 1 Summary statistics for estimates of lung and cervix cancer mortality.

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

Estimators Lung cancer Cervix cancer
  Mean Variance Min-max Mean Variance Min-Max
Observed rates 21.19 18.48 9.071–31.79 2.851 2.446 0.000–8.138
ATA Poisson kriging (PK) estimate 21.51 11.31 13.07–31.55 2.880 0.320 1.800–4.044
Point Poisson kriging (PK) estimate 21.52 11.13 13.33–31.61 2.897 0.338 1.811–4.061
BYM model estimate 21.38 11.35 13.96–31.63 2.889 0.200 1.915–4.074
ATA Poisson kriging (PK) variance 3.122 2.009 0.238–7.582 0.187 0.040 0.003–0.881
Point Poisson kriging (PK) variance 3.275 2.367 0.242–7.731 0.211 0.043 0.003–0.948
BYM model variance 3.224 3.288 0.306–12.05 0.154 0.009 0.004–0.509
Correlation PK vs ATA PK estimate 0.997    0.989   
Correlation BYM vs ATA PK estimate 0.988    0.845   
Correlation PK vs ATA PK variance 0.989    0.986   
Correlation BYM vs ATA PK variance 0.908    0.549   
  1. Mean, variance and range of cancer mortality rates per 100,000 person-years estimated at the county level using Poisson kriging (point and ATA) and BYM model. Statistics for the kriging variance and variance of Bayesian empirical distribution are listed below.