TY  - JOUR
PY  - 2004//
TI  - Joint Multivariate Statistical Model and Its Applications to Synthetic Earthquake Prediction
JO  - Acta seismologica sinica. English edition
A1  - Han, TX
A1  - Jiang, Chao
A1  - Wei, Xue-li
A1  - Han, M
A1  - Feng, De-yi
SP  - 578
EP  - 584
VL  - 17
IS  - 5
N2  - 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 sample 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 characterize and predict earthquakes in North China (30 deg–42 deg N, 108 deg–125 deg E) and better prediction results are obtained.     <p>Language: en</p>
LA  - en
SN  - 1000-9116
UR  - http://dx.doi.org/10.1007/s11589-004-0040-2
ID  - ref1
ER  -