Skip to main content

Table 3 Performance comparison of Bayesian and geostatistical estimators: mean absolute error of prediction.

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

Estimators

Lung cancer (mean = 21.25)

Cervix cancer (mean = 2.993)

Arithmetical average

Average

% best result

Average

% best result

Global Empirical Bayes

1.396

2

0.517

0

Local Empirical Bayes

1.380

0

0.458

0

BYM model

1.280

6

0.426

0

Point Poisson kriging (adjacent counties)

1.243

30

0.400

16

ATA Poisson kriging (adjacent counties)

1.246

32

0.397

14

ATA Poisson kriging (32 neighbors)

1.250

30

0.380

70

Population-weighted average

    

Global Empirical Bayes

0.972

2

0.152

0

Local Empirical Bayes

0.980

2

0.145

0

BYM model

0.918

12

0.141

4

Point Poisson kriging (adjacent counties)

0.903

32

0.133

18

ATA Poisson kriging (adjacent counties)

0.909

24

0.132

28

ATA Poisson kriging (32 neighbors)

0.911

28

0.132

50

  1. Results obtained on average (arithmetical and population-weighted) 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. Straightforward empirical Bayesian smoothers were also applied. Bold numbers refer to best performances. The second column gives the percentage of realizations where the particular method yields the smallest prediction error.