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

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.

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An Improved Grey Clustering Model with Multiattribute Spatial-Temporal Feature for Panel Data and Its Application

Mathematical Problems in Engineering ◽

10.1155/2020/6761597 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Jian Li ◽

Wan-ming Chen

Keyword(s):

Panel Data ◽

Data Clustering ◽

High Tech ◽

Absolute Amount ◽

Clustering Method ◽

Clustering Model ◽

Proposed Model ◽

Grey Clustering ◽

Feature Information ◽

Temporal Feature

Due to the complexity and uncertainty of the objective world and the limitation of cognition, it is difficult to extract the information and rules contained in the panel data effectively based on the traditional panel data clustering method. Given this, considering that the absolute amount level, increasing amount level, and volatility level are the main indicators to represent the spatial-temporal feature of the panel data, a novel grey clustering model with the multiattribute spatial-temporal feature of panel data is established, and then it is applied in the regional high-tech industrialization in China. The results show that the proposed model can make full use of the spatial-temporal feature information of the panel data, identify the problems existing in the clustering objects, and make the clustering results more objective and practical.

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An Adaptive Weighted Pearson Similarity Measurement Method for Load Curve Clustering

Energies ◽

10.3390/en11092466 ◽

2018 ◽

Vol 11 (9) ◽

pp. 2466 ◽

Cited By ~ 2

Author(s):

Rongheng Lin ◽

Budan Wu ◽

Yun Su

Keyword(s):

Data Clustering ◽

Clustering Algorithm ◽

Similarity Measurement ◽

Advanced Metering Infrastructure ◽

Load Curve ◽

Partial Similarity ◽

Clustering Model ◽

Curve Clustering ◽

Advanced Metering ◽

Power Provisioning

Load curve data from advanced metering infrastructure record the consumers’ behavior. User consumption models help one understand a more intelligent power provisioning and clustering the load data is one of the popular approaches for building these models. Similarity measurements are important in the clustering model, but, load curve data is a time series style data, and traditional measurement methods are not suitable for load curve data. To cluster the load curve data more accurately, this paper applied an enhanced Pearson similarity for load curve data clustering. Our method introduces the ‘trend alteration point’ concept and integrates it with the Pearson similarity. By introducing a weight for Pearson distance, this method helps to keep the whole contour of the load data and the partial similarity. Based on the weighed Pearson distance, a weighed Pearson-based hierarchy clustering algorithm is proposed. Years of load curve data are used for evaluation. Several user consumption models are found and analyzed. Results show that the proposed method improves the accuracy of load data clustering.

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Panel data clustering analysis based on composite PCC: a parametric approach

Cluster Computing ◽

10.1007/s10586-018-1973-x ◽

2018 ◽

Vol 22 (S4) ◽

pp. 8823-8833 ◽

Cited By ~ 5

Author(s):

Juan Yang ◽

Yuantao Xie ◽

Yabo Guo

Keyword(s):

Panel Data ◽

Clustering Analysis ◽

Data Clustering ◽

Parametric Approach

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Fitting partially linear functional-coefficient panel-data models with Stata

The Stata Journal Promoting communications on statistics and Stata ◽

10.1177/1536867x20976339 ◽

2020 ◽

Vol 20 (4) ◽

pp. 976-998

Author(s):

Kerui Du ◽

Yonghui Zhang ◽

Qiankun Zhou

Keyword(s):

Monte Carlo ◽

Panel Data ◽

Fixed Effects ◽

Linear Functional ◽

Semiparametric Estimation ◽

Data Models ◽

Panel Data Models ◽

Panel Models ◽

Partially Linear ◽

Functional Coefficient

In this article, we describe the implementation of fitting partially linear functional-coefficient panel models with fixed effects proposed by An, Hsiao, and Li [2016, Semiparametric estimation of partially linear varying coefficient panel data models in Essays in Honor of Aman Ullah ( Advances in Econometrics, Volume 36)] and Zhang and Zhou (Forthcoming, Econometric Reviews). Three new commands xtplfc, ivxtplfc, and xtdplfc are introduced and illustrated through Monte Carlo simulations to exemplify the effectiveness of these estimators.

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Panel Data Clustering and Its Application to Discount Rate of B Stock in China

2009 Second International Conference on Information and Computing Science ◽

10.1109/icic.2009.48 ◽

2009 ◽

Cited By ~ 3

Author(s):

Tao Zheng ◽

Donghua Zhu ◽

Xuefeng Wang ◽

Bo Yu

Keyword(s):

Panel Data ◽

Discount Rate ◽

Data Clustering

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Grey Clustering Evaluation for the Cooperation Efficiency of PPP Project: Taking Beijing Metro Line 4 as an Example

Mathematical Problems in Engineering ◽

10.1155/2019/8232731 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Hui Sun ◽

Yingzi Liang ◽

Yuning Wang

Keyword(s):

Public Sector ◽

Private Sector ◽

Evaluation Model ◽

Evaluation Index System ◽

The Public ◽

Clustering Model ◽

Grey Clustering ◽

Public Products ◽

Infrastructure Project

PPP model is an important model which provides public products or services based on the coordination between the public sector and private sector. The implementation of PPP model is helpful for relieving the stress of insufficient funding for public sector and improving the efficiency of resource allocation. Comparing with traditional infrastructure project, PPP project involves many stakeholders, and the cooperation efficiency during the different stakeholders impacts the results of the project directly. Thus, it is important to explore the cooperation efficiency of PPP project. Based on grey clustering model, this paper evaluates the cooperation efficiency of PPP project. An evaluation index system including 36 indexes is established based on the aims and objectives of three stakeholders (public sector, private sector, and passengers). A case study of Beijing Metro Line 4 PPP project is implemented to verify the validity and applicability of the evaluation model. And the results showed that the cooperation efficiency of Beijing Metro Line 4 PPP project is relatively high. The model also provided insights into the shortage of the cooperation efficiency of Beijing Metro Line 4 PPP project. As such, the results can assist all stakeholders in adjusting the cooperation efficiency.

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