Ranking of functional data in application to worldwide PM10 data analysis
Ranking of functional data in application to worldwide PM10 data analysis
复制标题
功能数据在全球 PM10 数据分析中的应用排名
DOI：
10.1007/s10651-017-0384-0
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发表时间：
2017
影响因子：
3.8
通讯作者：
Zhou Yingchun
中科院分区：
文献类型：
--
作者：
Lin Zhuhua;Zhou Yingchun
功能数据的排序对于进行进一步的基于排序的分析很重要。本文综述了几种常用的排序方法，如主成分分析/函数型主成分分析法和离散排序法，并提出了一种新的函数型数据排序方法：加权局部排序法。所提出的方法允许存在缺失值或在不匹配的时间点测量的值。它也有物理解释。仿真结果表明，该方法在各种情况下都具有较好的鲁棒性。在真实的数据分析中，将该方法应用于全球PM数据，生成秩，然后进行非参数秩和检验和基于秩的线性回归等进一步分析，得到有意义的结果。
Ranking of functional data is important for conducting further rank-based analysis. The paper reviews several ranking methods, such as the principal component analysis/functional principal component analysis and the discrete rank method, and proposes a new method for functional data: the weighted local rank method. The proposed method allows the presence of missing values or values measured at unmatched time points. It also has physical interpretation. All the methods are compared through simulation and the proposed method is more robust in various scenarios. In real data analysis, the proposed method is applied to worldwide PMdata to generate ranks, then further analysis such as nonparametric rank sum test and linear regression based on ranks are performed to produce meaningful results.