Research Article
Gaussian Mixture Models Based on Principal Components and Applications
Table 2
Simulation case: parameter estimates of the bivariate GMM (first technique) (the log-likelihood, BIC, and number of iterations are provided).
| Sample size | Model parameters | First component | Second component | | | | | | | | | | |
| 50 | 0.53806 | −0.19744 | 0.46146 | 2.68185 | 1.35652 | 0.46194 | 0.22997 | −0.53750 | 2.35390 | 0.30614 | | | Log-likelihood = −331.7415, BIC = 704.9295 Number of iterations: 70 |
| 100 | 0.38040 | 1.07288 | −0.6829 | 2.33042 | 1.04702 | 0.61959 | −0.6587 | 0.41927 | 1.46089 | 0.73289 | | | Log-likelihood = −332.917, BIC = 707.2805 Number of iterations: 96 |
| 500 | 0. 65985 | −0.48985 | −0.2038 | 2.10879 | 1.00732 | 0.34015 | 0.9503 | 0.39532 | 2.29243 | 0.96703 | | | Log-likelihood = −1678.241, BIC = 3379.508 Number of iterations: 77 |
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