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Semiparametric Estimation and Panel Data Clustering Analysis Based on D-Vine and C-Vine

机译:基于D-VINE和C-VINE的Semiparametric估算和面板数据聚类分析

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摘要

This paper proposed a panel data clustering model based on D-vine and C-vine and supported a semiparametric estimation for parameters. These models include a two-step inference function for margins, two-step semiparameter estimation, and stepwise semiparametric estimation. In similarity measurement, similarity coefficients are constructed by a multivariate Hierarchical Nested Archimedean Copula (HNAC) model and compound PCC models, which are HNAC and D-vine compound model and HNAC and C-vine compound model. Estimation solutions and models evaluation are given for these models. In the case study, the clustering results of HNAC and D-vine compound model and HNAC and C-vine compound model are given, and the effect of different copula families on clustering results is also discussed. The result shows the models are effective and useful.
机译:本文提出了一种基于D-VINE和C-VINE的面板数据聚类模型,并支持参数的半造型估计。这些型号包括用于边缘,两步半脉珠计估计和逐步半导体估计的两步推断功能。在相似性测量中,相似性系数由多变量分层嵌套ArchimeDean谱系(HNAAc)模型和化合物PCC型号构成,其是HNAc和D- vine化合物模型和HNAC和C- vine化合物模型。对这些模型给出了估计解决方案和模型评估。在案例研究中,给出了HNAC和D-葡萄化合物模型和HNAC和C-葡萄化合物模型的聚类结果,还讨论了不同拷贝家族对聚类结果的影响。结果表明模型是有效和有用的。

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