摘要
对于我国城市经济水平、环境水平的综合排序,目前已经有了比较完善的指标体系排序方法,但是其中涉及的大多都是多元数据.随着获取数据的方式增多和获取数据的技术日新月异,数据变得越来越复杂,某些领域所产生的观测数据不再是单纯的某一类数据,而是多种类型数据的组合.本文研究的就是当指标体系涉及到函数型数据时,该如何排序.对此,本文提出四种综合排序方法,并通过数值模拟对这些方法进行比较和选择,得到以下结论:当函数型数据受污染时,熵权法排序结果较稳定;当标量数据受污染时,多元修正带状深度排序方法更为稳定.研究表明,多类型数据排序方法的选择还需要根据原始数据的特征而定.该研究丰富了多类型数据的综合排序方法,具有很好的现实意义.
For the comprehensive ranking of urban economic level and environmental level in China,there have been some index system ranking methods, but most of them involve multivariate data. With the rapid change of data acquisition technology, data become more and more complex. The observational data produced in some fields is no longer a single type of data, but a combination of various types of data. This paper studies how to rank them when the index system involves functional data. In this paper, four ranking methods are proposed and compared. The results are as follows: when the functional data is contaminated, the entropy weight method results are relatively stable;when the scalar data is contaminated, the multivariate modified banding depth is more stable. The research shows that the selection of ranking methods for multi-type data depends on the characteristics of the data. This research enriches the comprehensive ranking of multi-type data and has good practical significance.
作者
尹腾腾
周迎春
YIN Tengteng;ZHOU Yingchun(School of Statistics,East China Normal University,Shanghai,200062,China)
出处
《应用概率统计》
CSCD
北大核心
2022年第3期357-378,共22页
Chinese Journal of Applied Probability and Statistics
基金
国家自然科学基金项目(批准号:11771146、11831008、81530086)资助.
关键词
多类型数据
排序
函数型分段排序加权法
数据深度
multi-type data
ranking
functional segments ranking weighted method
data depth
