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O. Sunanta:
"Generalized Point Estimators for Fuzzy Multivariate Data";
Austrian Journal of Statistics, Vol 47 (2018), No 1; S. 1 - 12.

Data analysis methods are necessary tools in evaluating and for better understanding the information of interest. However, there are limitations in applying standard statistical methods to data analysis in some cases. Data obtained from di erent sources are often
clouded by imprecision and uncertainty, also called fuzziness. To overcome this problem, data analysis has to be adapted and generalized through statistical methods for such fuzzy data to capture the uncertainty. These methods are largely based on the extension principle and/or require other generalized procedures for further calculation of statistics, e.g. the estimation of the unknown statistical parameters. The development of these methods speci cally for evaluating univariate data has been ourished. However, to solve
complex real-world problems, these methods have to be extended and generalized to handle multivariate fuzzy data. In this research, the methods of generalized point estimators, i.e. sample mean, variance-covariance, and correlation coe cient, are extended for the
multivariate case through concepts of fuzzy vector and combined fuzzy sample.

fuzzy multivariate data fuzzy vector combined fuzzy sample multivariate statistical analysis

http://dx.doi.org/10.17713/ajs.v47i1.391

https://www.ajs.or.at/index.php/ajs/article/view/vol47-1-2