Robustness Measurement of Comprehensive Evaluation Model Based on the Intraclass Correlation Coefficient

This study proposes a standardized robustness measurement framework for comprehensive evaluation models based on the Intraclass Correlation Coefficient (ICC(3,1)), The framework aims to address two key issues: (1) the non-unique evaluation results caused by the abundance of such models, and (2) the lack of standardization and the arbitrariness in existing robustness testing procedures. Theoretical derivation and simulation confirm that ICC(3,1) exhibits a positive correlation with Kendall’s Coefficient of Concordance (Kendall’s W) and a negative correlation with Root Mean Square Error (RMSE) and Normalized Inversion Index (NII), demonstrating superior stability, discrimination, and interpretability. Under increased noise levels, ICC(3,1) maintains a balance between robustness and sensitivity, supporting its application in robustness evaluation and method selection.

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Publication Details

Journal
Mathematics
Published
2025-05-25
DOI
https://doi.org/10.3390/math13111748
Citations
1
Primary Topic
Advanced Statistical Methods and Models
Type
article
Field-Weighted Citation Impact
2.63
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article

Robustness Measurement of Comprehensive Evaluation Model Based on the Intraclass Correlation Coefficient

Li Zhang, Shu Xian
1 citations
Mathematics
Advanced Statistical Methods and Models
2.63
article

Robustness Measurement of Comprehensive Evaluation Model Based on the Intraclass Correlation Coefficient

Li Zhang, Shu Xian
article en
1 citations

Abstract

This study proposes a standardized robustness measurement framework for comprehensive evaluation models based on the Intraclass Correlation Coefficient (ICC(3,1)), The framework aims to address two key issues: (1) the non-unique evaluation results caused by the abundance of such models, and (2) the lack of standardization and the arbitrariness in existing robustness testing procedures. Theoretical derivation and simulation confirm that ICC(3,1) exhibits a positive correlation with Kendall’s Coefficient of Concordance (Kendall’s W) and a negative correlation with Root Mean Square Error (RMSE) and Normalized Inversion Index (NII), demonstrating superior stability, discrimination, and interpretability. Under increased noise levels, ICC(3,1) maintains a balance between robustness and sensitivity, supporting its application in robustness evaluation and method selection.

MathematicsVol. 13(11)
Yunnan University (CN)
Openalex Percentile: Top 12%
Advanced Statistical Methods and Models
2.63
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