The behaviour of rank correlation coefficients for incomplete data

Crysdian, Cahyo ORCID: (2022) The behaviour of rank correlation coefficients for incomplete data. Research in Mathematics, 9 (1). pp. 1-14. ISSN 2768-4830

11914.pdf - Published Version
Available under License Creative Commons Attribution.

Download (2MB) | Preview


This paper presents the analysis to disclose the behaviour of rank correlation coefficients under the complete and incomplete data condition. The main concern of this research is to deal with the missing data by preserving the originality of data pair rather than experiencing data deletion or imputation. The paper introduces the variability function that is developed for each correlation coefficient in order to disclose the mean and the variance for every possible data sequences. The comparisons between Kendall, Spearman, and the absolute distance measure for index ranking demonstrate the use of variability function under both the complete and incomplete data, in which it becomes a useful tool to describe the coefficient’s mechanism to proceed with a set of possible data sequences. The analysis proves that Kendall coefficient becomes the better method compared to Spearman and the absolute distant measure due to threefold, i.e. the ability to preserve the zero mean of variability distribution in complete data, the ability to survive from the missing data, and the ability to gain a higher rate of convergence in incomplete condition. Meanwhile, Spearman fails to preserve the original data pair under the incomplete condition due to direct measurement of rank distances.

Item Type: Journal Article
Keywords: rank correlation coefficient; index ranking; incomplete data; missing data; variability function; variance estimate
Subjects: 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010405 Statistical Theory
08 INFORMATION AND COMPUTING SCIENCES > 0802 Computation Theory and Mathematics > 080299 Computation Theory and Mathematics not elsewhere classified
Divisions: Faculty of Technology > Department of Informatics Engineering
Depositing User: Dr. Cahyo Crysdian
Date Deposited: 09 Nov 2022 14:51


Downloads per month over past year

Origin of downloads

Actions (login required)

View Item View Item