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Table 2 Parameter estimates and 95% credible intervals for the joint model

From: Joint Bayesian modeling of time to malaria and mosquito abundance in Ethiopia

  Parameter Posterior mean 2.5% 97.5%
Abundance model     
Intercept β 0 0.66 0.40 0.91
S 1(r a i n(t)) β 1 4.40 4.18 4.61
S 2(r a i n(t)) β 2 0.92 0.85 0.99
Distance β 3 -0.19 -0.23 -0.12
Measurement error σ 0.71 0.70 0.71
Time to event model     
Age θ 1 0.01 -0.03 0.05
Gender θ 2 -0.04 -0.21 0.12
Association main effect α 1 0.12 0.04 0.19
Association interaction α 2 0.27 0.16 0.37
Hyper-parameters     
Penalty λ 0.005 0.002 0.008
Random effect covariance D 1,1 24.22 22.78 25.77
Random effect covariance D 2,1 0.94 0.60 1.27
Random effect covariance D 3,1 -0.73 -0.99 -0.47
Random effect covariance D 2,2 2.16 2.01 2.31
Random effect covariance D 3,2 0.81 0.72 0.91
Random effect covariance D 3,3 1.41 1.32 1.51
DIC 403478.6
  1. D i,j denote the ij-element of the covariance matrix for the random effects. Here rain is the only weather related covariate