Selected article for: "decisive evidence and richards model selection"
Author: Liu, Wendi; Tang, Sanyi; Xiao, Yanni
Title: Model Selection and Evaluation Based on Emerging Infectious Disease Data Sets including A/H1N1 and Ebola
  • Document date: 2015_9_15
    • ID: 0j4is0n4_49
    Snippet: In this subsection, we plan to realize the model selection procedures using the published accumulative cases number of A/H1N1 from the 8th Hospital of Xi'an, where the majority of the confirmed cases in the province of Shaanxi in early September 2009 were isolated. The selection results are given in the first line of Table 3 and Figure 2 (a). It follows from Table 3 that Bayes factorŝ1 2 ,̂1 3 are infinite (>100 naturally), which confirm that .....
    Document: In this subsection, we plan to realize the model selection procedures using the published accumulative cases number of A/H1N1 from the 8th Hospital of Xi'an, where the majority of the confirmed cases in the province of Shaanxi in early September 2009 were isolated. The selection results are given in the first line of Table 3 and Figure 2 (a). It follows from Table 3 that Bayes factorŝ1 2 ,̂1 3 are infinite (>100 naturally), which confirm that there exists decisive evidence for model 1 (i.e., Logistic model) compared with models 2 and 3 . Moreover, both 1 <̂1 4 = 1.34 < 3 and 1/3 < 41 = 0.75 < 1 mean that the selection of Logistic model and Richards model is uniform and alternating. To confirm the model selection results on A/H1N1 data set, we further calculate the AIC values which are given to be 249, 362, 592, and 254 for models 1 , 2 , 3 , and 4 , respectively. The AIC values for both Logistic model and Richards model support us to choose these two models, which are the best models for us to fit the A/H1N1 data.

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