Hypothesis testing of population parameters plays an important role in statistical analysis. This work extends this topic to the fuzzy environment for wider application. Developing a method of testing fuzzy hypotheses using linguistic variables is of priority concern. The method is based on Zadeh’s extension principle: the fuzzy population mean in the null hypothesis is converted to fuzzy numbe...

An information-based framework is presented for spatially explicit GIS-based ecological modeling. Within this framework some of the important issues and challenges of incorporating fuzzy sets in spatially explicit population models (SEPM) are discussed. Examples of current work are used to illustrate the main issues and challenges facing the incorporation of fuzzy sets in ecological modeling. A...

1 Ronei Marcos de Moraes, Department of Statistics, Federal University of Paraíba, Cidade Universitária s/n CEP 58.051-900 João Pessoa – PB Brazil, [email protected] 2 Liliane dos Santos Machado, Department of Informatics, Federal University of Paraíba, Cidade Universitária s/n CEP 58.051-900 João Pessoa – PB Brazil, [email protected] Abstract  This paper presents a methodology of evaluation o...

Statistical hypothesis testing is very important for finding decisions in practical problems. Usually, the underlying data are assumed to be precise numbers, but it is much more realistic in general to consider fuzzy values which are non-precise numbers. In this case the test statistic will also yield a non-precise number. This article presents an approach for statistical testing at the basis o...

consider an exchangeable normal vector with parameters ????2, and ?. on the basis of a vector observation some tests about these parameters are found and their properties are discussed. a simulation study for these tests and a few nonparametric tests are presented. some advantages and disadvantages of these tests are discussed and a few applications are given.

Abstract: A fuzzy test for testing statistical hypotheses about an imprecise parameter is proposed for the case when the available data are also imprecise. The proposed method is based on the relationship between the acceptance region of statistical tests at level β and confidence intervals for the parameter of interest at confidence level 1 − β. First, a fuzzy confidence interval is constructe...