Panel data clustering analysis based on composite PCC: a parametric approach
- 82 Downloads
This paper proposed a panel data clustering model based on Hierarchical Nested Archimedean Copula (HNAC) model and compound PCC models. The method provides a new approach to panel data clustering, which breaks through the limitations of the traditional data clustering and time series clustering. This article makes full use of the dependence structure between the sectional individuals, as well as the degree of correlation between time series data. The similar structure was constructed by HNAC and Pair Copula to reflect the change of the clustering results. The selection of Copula clusters is very flexible giving the clustering results more accurate, robust, and easily interpreted. The computing efficiency is high and the estimation for the goodness-of-fit test are given based on compound PCC method in this paper. In the case study, the clustering results of compound PCC models are excellent. The result shows that the compound PCC models are effective and useful.
KeywordsPanel data clustering Pair copula construction (PCC) Hierarchical Nested Archimedean Copula
The research is supported by the National Natural Science Foundation Project of China (No. 71303045)and the Fundamental Research Funds for the Central Universities in UIBE (CXTD9-04), “Research on the growth direction and path of economic new kinetic energy driven by scientific and technological innovation” (ZLY201703) and “Research on the strategic direction and path of modern economic system driven by scientific and technological innovation” (ZLY201734).
- 2.Basu, S., Banerjee, A., Mooney, R.: Semi-supervised clustering by seeding. In: Machine Learning Proceedings of the Nineteenth International Conference (ICML 2002), pp. 27–34 (2002)Google Scholar
- 10.Xie, Y.T., Yang, J.: Panel data clustering analysis based on density. Stat. Inf. Forum 40–53. (2014) In ChineseGoogle Scholar
- 11.Xie, Y.T., Yang, J., Liu, H.Y.: Agriculture risk regionalization analysis based on panel data clustering with affinity propagation. Stat. Inf. Forum 34–54. (2017) In ChineseGoogle Scholar
- 12.Guan, X., Zeng, W., Wang, N.: MRI data analysis of affinity propagation clustering based on similarity matrix reduction. Comput. Eng. 117–134 (2016)Google Scholar