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Table 2 The Parameter estimates of the tentative models with their AIC and SC

From: Statistical methods for predicting tuberculosis incidence based on data from Guangxi, China

Model Variable Coefficient Std. Error t-Statistic prob AIC SC
SARIMA(1,0,1)(0,1,0)12 C −0.81 0.16 −4.92 < 0.01   
AR(1) −0.16 0.22 −0.73 0.47 3.00 3.10
MA(1) 0.51 0.20 2.52 0.01   
SARIMA(1,0,(2))(0,1,0)12 C −0.84 0.21 −4.00 < 0.01   
AR(1) 0.23 0.12 1.93 0.06 3.03 3.12
MA(2) 0.27 0.12 2.23 0.03   
SARIMA((2),0,1)(0,1,0)12 C −0.78 0.20 −3.96 < 0.01   
AR(2) 0.14 0.12 1.21 0.23 2.94 3.04
MA(1) 0.37 0.12 3.15 < 0.01   
SARIMA((2),0,(2))(0,1,0)12 C −0.77 0.15 −5.22 < 0.01   
AR(2) −0.59 0.10 −6.07 < 0.01 2.87 2.98
MA(2) 0.96 0.02 54.39 < 0.01   
SARIMA(2,0,(2))(0,1,0)12 C −0.78 0.16 −4.83 < 0.01   
AR(1) 0.17 0.10 1.77 0.08 2.87 3.00
AR(2) −0.61 0.10 −6.26 < 0.01   
MA(2) 0.97 0.02 46.44 < 0.01   
SARIMA(2,0,1)(0,1,0)12 C −0.71 0.22 −3.21 0.00   
AR(1) 0.64 0.25 2.57 0.01 2.91 3.03
AR(2) −0.02 0.14 −0.17 0.86   
MA(1) −0.31 0.27 −1.15 0.26   
SARIMA(1,0,2)(0,1,0)12 C −0.81 0.18 −4.46 < 0.01   
AR(1) −0.16 0.29 −0.54 0.59 3.01 3.13
MA(1) 0.50 0.30 1.65 0.10   
MA(2) 0.18 0.14 1.29 0.20   
SARIMA(2,0,2)(0,1,0)12 C −0.72 0.22 −3.31 < 0.01   
AR(1) 0.65 0.26 2.52 0.01   
AR(2) −0.09 0.21 − 0.44 0.66 2.93 3.09
MA(1) −0.33 0.28 −1.17 0.25   
MA(2) 0.12 0.23 0.52 0.60