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

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.

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

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Hong Li ◽  
Yuantao Xie ◽  
Juan Yang ◽  
Di Wang
Keyword(s):  
Panel Data ◽  
Clustering Analysis ◽  
Data Clustering ◽  
Semiparametric Estimation ◽  
Similarity Measurement ◽  
Model Estimation ◽  
Similarity Coefficients ◽  
Clustering Model ◽  
Compound Model

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.

Three way decisions based grey incidence analysis clustering approach for panel data and its application

Kybernetes ◽  
2019 ◽  
Vol 48 (9) ◽  
pp. 2117-2137 ◽  
Author(s):  
Yong Liu ◽  
Jun-liang Du ◽  
Ren-Shi Zhang ◽  
Jeffrey Yi-Lin Forrest
Keyword(s):  
Panel Data ◽  
Data Clustering ◽  
Threshold Value ◽  
Content Type ◽  
Clustering Problem ◽  
Proposed Model ◽  
Clustering Approach ◽  
Grey Incidence Analysis ◽  
Incidence Analysis ◽  
Extract Information

PurposeThis paper aims to establish a novel three-way decisions-based grey incidence analysis clustering approach and exploit it to extract information and rules implied in panel data.Design/methodology/approachBecause of taking on the spatiotemporal characteristics, panel data can well-describe and depict the systematic and dynamic of the decision objects. However, it is difficult for traditional panel data analysis methods to efficiently extract information and rules implied in panel data. To effectively deal with panel data clustering problem, according to the spatiotemporal characteristics of panel data, from the three dimensions of absolute amount level, increasing amount level and volatility level, the authors define the conception of the comprehensive distance between decision objects, and then construct a novel grey incidence analysis clustering approach for panel data and study its computing mechanism of threshold value by exploiting the thought and method of three-way decisions; finally, the authors take a case of the clustering problems on the regional high-tech industrialization in China to illustrate the validity and rationality of the proposed model.FindingsThe results show that the proposed model can objectively determine the threshold value of clustering and achieve the extraction of information and rules inherent in the data panel.Practical implicationsThe novel model proposed in the paper can well-describe and resolve panel data clustering problem and efficiently extract information and rules implied in panel data.Originality/valueThe proposed model can deal with panel data clustering problem and realize the extraction of information and rules inherent in the data panel.

Grey Clustering Model of Coating Erosion Resistance Evaluation

2012 ◽  
Vol 602-604 ◽  
pp. 2263-2266
Author(s):  
Bao Ping Chen
Keyword(s):  
Erosion Resistance ◽  
Evaluation Process ◽  
Comparison Method ◽  
The Other ◽  
Clustering Method ◽  
Resistance Evaluation ◽  
Accurate Evaluation ◽  
Clustering Model ◽  
Grey Clustering ◽  
Data Volume

The erosion resistance evaluation of coating has important academic significance as well as project application value. Being the traditional way, the direct comparison method requires large amount of trivial work. By taking different coatings as clustering objects and data obtained from coating in erosion experiments as clustering index, this paper, on the other hand, defines the whitenization weight function of the index with grey clustering method, and has in this way built the grey clustering mathematical model of coating erosion resistance evaluation. As is proved in practice that the model makes it possible to evaluate the erosion resistance of coating scientifically, making the evaluation more reasonable and objective. At the same time, due to the accurate evaluation results, it avoids the subjectivity and one-sidedness otherwise being unavoidable for judging by experience. The coating erosion resistance experiments do not have strict demands for data volume, which makes the evaluation process both simple and scientific.

Quantitative analysis of the cities’ innovation capacity based on grey variable weight clustering

2015 ◽  
Vol 5 (3) ◽  
pp. 344-353
Author(s):  
Yeqing Guan ◽  
Hua Liu ◽  
Ying Zhu
Keyword(s):  
Quantitative Analysis ◽  
Weight Function ◽  
Design Methodology ◽  
Clustering Method ◽  
Content Type ◽  
Clustering Model ◽  
Variable Weight ◽  
Grey Clustering ◽  
Innovation Capacity

Purpose – The purpose of this paper is to find the reason which the results of grey variable weight clustering method do not correspond with the reality. It proposes reconstructing the whitenization weight function, outlining why and how inconsistency is avoided. The study aims to improve the model of grey clustering method based on the whitenization weight function and list the steps of the new clustering model so that analysis and application of innovation capacity in a broader range is normally found. Design/methodology/approach – First the reason for the problem that the clustering results of grey variable weight clustering do not correspond with the reality is analyzed in two existing literature. And then a new whitenization weight function is reconstructed, two properties of the whitenization weight function are proved. The solution of the new grey variable weight clustering based on the whitenization weight function is built by following six steps. Findings – The paper provides a new whitenization weight function which satisfies the normative and non-triplecrossing. It suggests that successful clustering results of innovation capacity act on two levels: integrating the elements of innovation capacity indexes, and following steps of grey variable weight clustering. Originality/value – This paper improves the existing method of grey variable weight clustering and fulfills an identified need to study how cities’ innovation capacity can be clustered.

Improvement of Grey Clustering Model for Comprehensive Assessment of Environmental Quality: A Case Study in Water Environment

2011 ◽  
Vol 356-360 ◽  
pp. 2222-2227
Author(s):  
Liang Qian Fan ◽  
Qing Yu Zhang ◽  
Qi Liu ◽  
Zu Cheng Wu ◽  
Bo Yu ◽  
...  
Keyword(s):  
Environmental Quality ◽  
Water Environment ◽  
Comprehensive Assessment ◽  
Clustering Method ◽  
Modified Method ◽  
Clustering Model ◽  
Weight Problem ◽  
Grey Clustering ◽  
Tiaoxi River

The comprehensive assessment of environmental quality is useful for comparison of environmental quality and identification of pollution trends. In the comprehensive assessment methods, many studies proved that grey clustering method is accurate and effective. However, the zero-weight problem occurs in the traditional grey clustering method (TGCM). This flaw may lead to the assessment result distortion. To solve this problem, this paper proposes a modified grey clustering method (MGCM) in which the linear whitening functions in the traditional grey clustering method is replaced by exponential functions. The modified method was applied to assess water quality of four monitoring sections in the Yuhang section of the Tiaoxi River. Then, the results were compared with those obtained with the TGCM. The comparisons show that solving the zero-weight problem can lead to different assessment results and the MGCM is effective to solve the zero-weight problem. The MGCM is more accurate than the TGCM.

scholarly journals Estimating latent group structure in time-varying coefficient panel data models

2019 ◽  
Author(s):  
Jia Chen
Keyword(s):  
Panel Data ◽  
Data Models ◽  
Panel Data Models ◽  
Time Varying ◽  
Clustering Method ◽  
Varying Coefficient ◽  
Varying Coefficients ◽  
Time Varying Coefficients ◽  
Latent Group ◽  
Group Structures

Summary This paper studies the estimation of latent group structures in heterogeneous time-varying coefficient panel data models. While allowing the coefficient functions to vary over cross-sections provides a good way to model cross-sectional heterogeneity, it reduces the degree of freedom and leads to poor estimation accuracy when the time-series length is short. On the other hand, in a lot of empirical studies, it is not uncommon to find that heterogeneous coefficients exhibit group structures where coefficients belonging to the same group are similar or identical. This paper aims to provide an easy and straightforward approach for estimating the underlying latent groups. This approach is based on the hierarchical agglomerative clustering (HAC) of kernel estimates of the heterogeneous time-varying coefficients when the number of groups is known. We establish the consistency of this clustering method and also propose a generalised information criterion for estimating the number of groups when it is unknown. Simulation studies are carried out to examine the finite-sample properties of the proposed clustering method as well as the post-clustering estimation of the group-specific time-varying coefficients. The simulation results show that our methods give comparable performance to the penalised-sieve-estimation-based classifier-LASSO approach by Su et al. (2018), but are computationally easier. An application to a panel study of economic growth is also provided.

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

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
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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