CRITIC Weight Calculation Method Based on Quartiles and Improved Distance Correlation Coefficient and Its Applications.

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Bibliographic Details
Title:	CRITIC Weight Calculation Method Based on Quartiles and Improved Distance Correlation Coefficient and Its Applications.
Authors:	Xie, Ailing¹ 906463038@qq.com, Chang, Huilong¹ 1209685683@qq.com, Li, Chaonan¹ 3066969239@qq.com, Zou, Limin² 307376216@qq.com
Source:	Engineering Letters. Jul2026, Vol. 34 Issue 7, p2764-2774. 11p.
Subjects:	Quantiles, Statistical correlation, Robust statistics, Statistical measurement, Multiple criteria decision making
Abstract:	In multi-criteria decision-making, the determination of weights is a critical step, as it has a direct and significant impact on the rationality and accuracy of the decision results. The traditional CRITIC method for weight calculation utilizes the standardized deviations, after dimensionless normalization, to reflect the variability among the indicators. This paper analyzes two case studies to highlight the limitations of using standardized deviations after dimensionless normalization to measure the variability among indicators, and expounds the reasons why the existing literature uses the coefficient of variation to improve the CRITIC weight calculation method. At the same time, we observed that both the coefficient of variation and the distance correlation coefficient are sensitive to outliers, leading to unstable weight calculation results and low robustness. In light of this, this paper proposes a CRITIC weight calculation method based on quartiles and improved distance correlation coefficient (QD-CRITIC). The proposed method first measures the variability among the indicators using the ratio of the interquartile range to the median. It then calculates the conflict among indicators using improved distance correlation coefficient. Furthermore, six different weight assignment methods are applied to the Iris dataset to compute the weights of the indicators, and a comparative analysis of the resulting weights is conducted. Finally, the proposed weight calculation method is applied to real-world cases in the natural gas and surface water industries. To validate the effectiveness of the proposed method, a comparative analysis is conducted with five other commonly used weight assignment methods. The results show that the weights calculated using the proposed method are consistent with the actual situation and demonstrate strong robustness. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	In multi-criteria decision-making, the determination of weights is a critical step, as it has a direct and significant impact on the rationality and accuracy of the decision results. The traditional CRITIC method for weight calculation utilizes the standardized deviations, after dimensionless normalization, to reflect the variability among the indicators. This paper analyzes two case studies to highlight the limitations of using standardized deviations after dimensionless normalization to measure the variability among indicators, and expounds the reasons why the existing literature uses the coefficient of variation to improve the CRITIC weight calculation method. At the same time, we observed that both the coefficient of variation and the distance correlation coefficient are sensitive to outliers, leading to unstable weight calculation results and low robustness. In light of this, this paper proposes a CRITIC weight calculation method based on quartiles and improved distance correlation coefficient (QD-CRITIC). The proposed method first measures the variability among the indicators using the ratio of the interquartile range to the median. It then calculates the conflict among indicators using improved distance correlation coefficient. Furthermore, six different weight assignment methods are applied to the Iris dataset to compute the weights of the indicators, and a comparative analysis of the resulting weights is conducted. Finally, the proposed weight calculation method is applied to real-world cases in the natural gas and surface water industries. To validate the effectiveness of the proposed method, a comparative analysis is conducted with five other commonly used weight assignment methods. The results show that the weights calculated using the proposed method are consistent with the actual situation and demonstrate strong robustness. [ABSTRACT FROM AUTHOR]
ISSN:	1816093X