Fuzzy models in regional statistics

Sunanta, Owat and Viertl, Reinhard (2016): Fuzzy models in regional statistics. Published in: Regional Statistics , Vol. 6, No. 1 (2016): pp. 104-118.

Abstract

Many regional data are not provided as precise numbers, but they are frequently non-precise (fuzzy). In order to provide realistic statistical information, the imprecision must be described quantitatively. This is possible using special fuzzy subsets of the set of real numbers ℝ, called fuzzy numbers, together with their characterising functions. In this study, the uncertainty of measured data is highlighted through an example of environmental data from a regional study. The generalised statistical methods, through the characterising function and the δ-cut, that are suitable for the situations of fuzzy uni- and multivariate data are described. In addition, useful generalised descriptive statistics and predictive models frequently applicable for analysis of fuzzy data in regional studies as well as the concept of fuzzy data in databases are presented.

• Item Type: MPRA Paper
• Language: English
• Keywords: fuzzy data in regional studies, characterising function, statistics with fuzzy data, fuzzy data in databases
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C46 - Specific Distributions ; Specific Statistics; P - Economic Systems > P2 - Socialist Systems and Transitional Economies > P25 - Urban, Rural, and Regional Economics; R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R1 - General Regional Economics > R11 - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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