砂基液化的因素筛选及预测模型
The Model on Factors Selection and Prediction of Sand Liquefaction

作者: 崔栋利 , 牟唯嫣 :北京建筑大学理学院,北京;

关键词: 砂基液化因子分析判别分析Sand Liquefaction Factor Analysis Discriminant Analysis

摘要: 为降低数据维数,简化数据运算,我们采用因子分析和判别分析相结合的方法,运用方差累计贡献率在85%以上的前k个主成分代替原始砂基液化的有关因素,对砂基液化因素进行分析,这种方法并没有缩减样本量,只是对原始数据进行了浓缩和综合,通过对得到的因子得分数据进行判别分析,可得到一组判别结果。另外,利用因子分析的提取方法得到变量的共同度,变量共同度高的表示变量中的大部分信息均能够被因子所提取,选出变量共同度较高的对应的变量,利用这些变量再次进行判别分析,对两次判别分析得到的结果与原结果进行汇总对比,分析误判率。结果表明,这两种方法的结合在一定程度上用于筛选砂基液化的主要因素以及预测砂基液化可行性强,效果较好。

Abstract: In order to reduce data dimension, simplify data operation, we adopted the method combining the factor analysis and discriminant analysis, and applied the cumulative variance contribution rate of k in front of more than 85% of the principal components instead of the original related factors of sand liquefaction to analyze, this method didn’t reduce sample size, just made the raw data enrich- ment and comprehensive, did the discriminant analysis based on the factor score data, a set of discriminant results can be obtained. In addition, the extraction methods of principle component analysis were used to get the variable joint degrees, high variable joint degrees indicated the most information can be extracted by factor, then found the corresponding variable, did the discriminant analysis using these variables again, the two discriminant analysis results were compared with the original results and analyzed the misjudgment rate. Results show that the combination of the two methods has strong feasibility in filtering the main factors of sandlique faction and the prediction of sand liquefaction to some extent, and the effect is better.

文章引用: 崔栋利 , 牟唯嫣 (2015) 砂基液化的因素筛选及预测模型。 统计学与应用, 4, 312-318. doi: 10.12677/SA.2015.44035

参考文献

[1] 李学文. 中国袖珍百科全书[M]. 北京: 长城出版社, 2001: 5301-5309.

[2] 陈胜可. 统计分析从入门到精通[M]. 北京: 清华大学出版社, 2013: 349-360.

[3] 王鹏泽, 刘鹏飞, 等. 因子、聚类及判别分析在烟叶风格特色评价中的应用[J]. 中国烟草科学, 2015, 36(2): 20-25.

[4] 邵良杉, 徐波. 基于因子分析与Fisher判别分析法的隧洞围岩分类研究[J]. 公路交通科技, 2015, 32(7): 98-100.

[5] 王玉杰, 王千. 主要土壤肥力因素指标的筛选模型[J]. 生物数学学报, 2000, 15(2): 163-168.

[6] 梅长林, 范金城. 数据分析方法[M]. 北京: 高等出版社, 2006: 142-164.

[7] 何晓群. 多元统计分析[M]. 北京: 中国人民大学出版社, 2012: 143-154.

分享
Top