铁路货物周转量的半参数回归模型预测  

Forecast of Railway Freight Ton-Kilometers Based on Semi-parametric Regression

作　　者：王如义[1] 王慈光[1] 郭孜政[1] 唐建桥[1] 

机构地区：[1]西南交通大学交通运输学院,四川成都610031

出　　处：《西南交通大学学报》2008年第1期96-100,共5页Journal of Southwest Jiaotong University

摘　　要：为提高铁路货物周转量预测的准确性,在定性分析的基础上,运用灰色关联度理论选择出反映铁路运输供给能力的7个因素,并用偏最小二乘回归方法处理变量的共线性问题.采用非参数方法表达不能量化的影响因素,建立了半参数回归模型,并与线性回归模型和灰色预测模型进行了比较.研究结果表明,用半参数回归模型预测铁路货物周转量,预测结果的相对误差仅1.7%,比线性回归模型和灰色预测模型的预测精度更高.To raise the forecast precision of railway freight ton-kilometers （RFTK）, seven factors reflecting the supply capacity of railway transportation were selected out based on a qualitative analysis and the grey relevancy degree theory, and a semi-parametric regression model was established. The partial least-squares regression method was used to process the multicoUinearity of variables, i.e. , the seven factors, and the non-parameter method was applied to express qualitative factors. The research result shows that with the established seml-parametric regression model, the least relative error of the forecast result for RFTK is only 1.7%, and compared with the linear regression model and the grey forecast model, the semi-parametric regression model has a better effect and higher precision for the forecast of railway freight ton-kilometers.

关 键 词：铁路运输 周转量预测 半参数回归 偏最小二乘回归方法 非参数方法 

分 类 号：F502[经济管理—产业经济]