SA  >> Vol. 4 No. 3 (September 2015)

    The Application of Variable Selection to Multi-Collinearity Problems—Based on the Research and Development Input and Output Data

  • 全文下载: PDF(539KB) HTML   XML   PP.133-143   DOI: 10.12677/SA.2015.43015  
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安蕾,贾慧芝:云南财经大学统计与数学学院,云南 昆明

变量选择多重共线性岭回归PLS回归Variable Selection Multi-Collinearity Ridge Regression Partial Least-Squares regression



A prerequisite for the promotion of a nation’s innovation ability is the input of scientific research, but there are always many multi-collinearity problems among the indexes. In order to know the R&D input-output mode, 31 provinces are divided into two parts to set up ridge regression and PLS regression models separately. The research results show that different areas are influenced by different factors. The Midwest is susceptible to the input of the government and companies, while the technological innovation consciousness of the enterprises in the developed area is stronger.

安蕾, 贾慧芝. 变量选择方法在多重共线性问题中的应用—基于全国科技投入产出数据的实例[J]. 统计学与应用, 2015, 4(3): 133-143.


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