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Joint multivariate statistical model and its applications to the synthetic earthquake prediction
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作者 HAN Tian-xi(韩天锡) +7 位作者 JIANG Chun(蒋淳) WEI Xue-li(魏雪丽) HAN Me(韩梅) FENG De-yi(冯德益) 《Acta Seismologica Sinica(English Edition)》 CSCD 2004年第5期578-584,共8页
Considering the problems that should be solved in the synthetic earthquake prediction at present, a new model is proposed in the paper. It is called joint multivariate statistical model combined by principal component... Considering the problems that should be solved in the synthetic earthquake prediction at present, a new model is proposed in the paper. It is called joint multivariate statistical model combined by principal component analysis with discriminatory analysis. Principal component analysis and discriminatory analysis are very important theories in multivariate statistical analysis that has developed quickly in the late thirty years. By means of maximization information method, we choose several earthquake prediction factors whose cumulative proportions of total sam-ple variances are beyond 90% from numerous earthquake prediction factors. The paper applies regression analysis and Mahalanobis discrimination to extrapolating synthetic prediction. Furthermore, we use this model to charac-terize and predict earthquakes in North China (30~42N, 108~125E) and better prediction results are obtained. 展开更多
关键词 joint multivariate statistical model principal component analysis discriminatory analysis syn-thetic earthquake predication
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