Evaluation of China’s Financial Risk Using Projection Pursuit Clustering Model and Its Positive Research

Lou, Jitong; Lou, Wengao; Yu, Xiurong

doi:10.7763/joebm.2014.v2.114

Cited by 2 publications

(8 citation statements)

References 7 publications

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“…On the other hand, projection pursuit clustering (PPC) technology can be applied to the modeling of high-dimensional nonlinear and abnormal distribution data [29][30][31][32][33][34][35]. e basic concept of PPC modeling is completely consistent with the human thinking mode of comprehensive evaluation, ranking, and classification and has been widely used in the fields of management science, social science, natural science, and so on.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It is very difficult to get the real optimal of the model (1). As a rule, metaheuristic algorithms that are nature inspired are prominent because of their considerable and efficient performance when it comes to dealing with projection index function that is complex; this has been observed in the past two decades [23][24][25][29][30][31][32][33][34].…”

Section: E Principle and Characters Of Parasitism-predationmentioning

confidence: 99%

“…Although the metaheuristic algorithms such as PPA possess the search capability and get their optimal, there is no guarantee that the global optimal will be reached every time. Lou et al [30,31] proposed three theorems and two inferences to judge whether the optimization process reaches the real global optimal or not, which are as follows.…”

Section: E Principle and Characters Of Parasitism-predationmentioning

confidence: 99%

“…With the above-mentioned three theorems and two inferences, by changing the normalization mode of some indicators or adding some dummy indicators with equal values, we can judge whether the optimization process reaches the real global optimal or not. e more detailed proof processes are referred to [30,31].…”

Section: Theorem 3 If the Constraint Is That All Indicators' Weights Must Be Equal Or Greater Than Zeroes A Negative Indicator's Optimal mentioning

confidence: 99%

“…According to the theorems and inferences proposed by Lou et al [30,31] and taking R � r max /5, the global optimal solution of the PPC model is obtained, and the optimal weights of the indicators are a → a(1), a( 2 17), S (19) are less than zero, indicating that these 12 indicators are "positive indicators" in theory, but in reality, these indicators are negatively correlated with other indicators. is indicates that the "theoretical attribute" of these indicators is inconsistent with their "actual attribute."…”

Section: To Establish a Ppc Model For Comprehensive Evaluation Of Tourism Economy Vulnerability In Provincesmentioning

confidence: 99%

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Vulnerability Assessment and Spatiotemporal Differentiation of Provinces Tourism Economic System Based on the Projection Pursuit Clustering Model

Wang

2021

Discrete Dynamics in Nature and Society

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The vulnerability assessment indicator system (VAIS), including the tourism economic sensitivity and respondence, is modified and established in this paper. According to the collected data, during 2014–2018 of the 31 provinces of China, of the tourism economy sensitivity and respondence, the improved comprehensive evaluation projection pursuit clustering (PPC) model is established, and the vulnerability indexes of the 31 provinces are calculated, thus expanding the tourism economic vulnerability assessment methods. Our empirical results show that, during the period of 2014 to 2018, the sensitivity, the respondence, and the vulnerability indexes are unbalanced overall. The tourism economy sensitivity and the respondence show that the spatiotemporal distribution characteristics are high in the east and low in the west. On the contrary, as for the vulnerability, the spatiotemporal distribution characteristics are low in the east and high in the west. Among the 40 indicators, the ratio of industrial solid waste utilized (%), urbanization rate, and the density of grade highway and railway network (km/km2) have the greatest impact on the respondence, while the proportion of the population affected by natural disasters, the diversification index of industrial structure, and the number of traffic accident casualties have the most significant impact on the sensitivity, which are the indicators that have the greatest impact on vulnerability. Therefore, in order to effectively reduce sensitivity, improve respondence, and thus reduce the vulnerability index of the tourism economy, the provinces should first improve the above-mentioned evaluation indicators with the largest weights. Our research results in this paper enrich the theory of sustainable development of the tourism industry and derive managerial and policy insights for further achieving the high-quality development of the tourism economy.

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Section: Literature Reviewmentioning

confidence: 99%

Section: E Principle and Characters Of Parasitism-predationmentioning

confidence: 99%

Section: E Principle and Characters Of Parasitism-predationmentioning

confidence: 99%

Section: Theorem 3 If the Constraint Is That All Indicators' Weights Must Be Equal Or Greater Than Zeroes A Negative Indicator's Optimal mentioning

confidence: 99%

Section: To Establish a Ppc Model For Comprehensive Evaluation Of Tourism Economy Vulnerability In Provincesmentioning

confidence: 99%

See 3 more Smart Citations

Vulnerability Assessment and Spatiotemporal Differentiation of Provinces Tourism Economic System Based on the Projection Pursuit Clustering Model

Wang

2021

Discrete Dynamics in Nature and Society

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A Projection Pursuit Dynamic Cluster Model for Tourism Safety Early Warning and Its Implications for Sustainable Tourism

Zhong,

Lou,

Lai

2023

Mathematics

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According to the United Nations World Tourism Organization, tourism promotes sustainable economic development. Ensuring tourism safety is an essential prerequisite for its sustainable development. In this paper, based on the three evaluation index systems for tourism safety early warning and the collected sample data, we establish three projection pursuit dynamic cluster (PPDC) models by applying group search optimization, a type of swarm intelligence algorithm. Based on case studies, it is confirmed that the results derived from the PPDC models are consistent with the expert judgments. The importance of the evaluation indicators can be sorted and classified according to the obtained optimal projection pursuit vector coefficients, and the tourism risks of the destinations can be ranked according to the sample projection values. Among the three aspects influencing tourism safety in case one, the stability of the tourism destination has the most significant impact, followed by the frequency of disasters. Of the ten evaluation indicators, the frequency of epidemic disease affects tourism safety the most, and the unemployment ratio affects it the second most. Overall, the PPDC model can be adopted for tourism safety early warning with high-dimensional non-linear and non-normal distribution data modeling, as it overcomes the “curse of dimensionality” and the limitations associated with small sample sizes.

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Evaluation of China’s Financial Risk Using Projection Pursuit Clustering Model and Its Positive Research

Cited by 2 publications

References 7 publications

Vulnerability Assessment and Spatiotemporal Differentiation of Provinces Tourism Economic System Based on the Projection Pursuit Clustering Model

Vulnerability Assessment and Spatiotemporal Differentiation of Provinces Tourism Economic System Based on the Projection Pursuit Clustering Model

A Projection Pursuit Dynamic Cluster Model for Tourism Safety Early Warning and Its Implications for Sustainable Tourism

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