Tham khảo tài liệu 'data analysis machine learning and applications episode 1 part 5', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Model Selection in Mixture Regression Analysis 65 Suppose a researcher has the following prior probabilities to observe one of the models P1 P2 and p3 the proportional chance criterion for each factor level combination is CMprop and the maximum chance criterion is CMmax . The following figures illustrate the findings of the simulation run. Line charts are used to show the success rates for all sample segment size combinations. Vertical dotted lines illustrate the boundaries of the previously mentioned chance models with K M1 M2 M3 CMran lower dotted line CMprop medial dotted line and CMmax upper dotted line . These boundaries are just exemplary and need to be specified by the researcher in dependence of the analysis at hand. Figure 1 illustrates the success rates of the five information criteria with re- Fig. 1. Success rates with minor mixture proportions spect to minor mixture proportions. Whereas AIC demonstrates a poor performance across all levels of sample size CAIC outperforms the other criteria across almost all factor levels. The criterion performs favorably in recovering the true number of segments meeting exemplary chance boundaries for sample sizes of approximately 150 random chance proportional chance and 250 maximum chance respectively. The results in figure 2 from intermediate and near-uniform mixture proportions confirm the previous findings and underline the CAIC s strong performance in small sample size situations quickly achieving success rates of over 90 . However as sample sizes increase to 400 both ABIC and AIC3 perform advantageously. Even with near-unifrom mixture proportions AIC fails to any meet chance boundaries used in this set-up. In contrast to previous findings by Andrews and Currim 2003b CAIC outperforms BIC across almost all sample segment size combinations whereupon the deviation is marginal in the minor mixture proportion case. 66 Marko Sarstedt and Manfred Schwaiger Fig. 2. Success rates with .
