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O. Sunanta:
"Generalized Point Estimators for Fuzzy Multivariate Data";
Report No. Forschungsbericht ASTAT-2016-2, Dezember, 2016; 11 pages.

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 specific data analysis. Data obtained from different sources are often clouded by imprecision and uncertainty. To overcome this problem, data analysis methods have to be generalized through statistical methods for fuzzy data to capture the uncertainty. These methods are largely based on the extension principle or require other generalized procedures, such as the calculation of statistics, the estimation of parameters.
The development of these methods specially for evaluating univariate data has been flourished. 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 coefficient, 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://institute.tuwien.ac.at/astat/forschung/