Asymptotic and Bootstrap techniques for testing the expected value of a fuzzy random variable

Abstract.

In this paper we will consider hypothesis-tests for the (fuzzy-valued) mean value of a fuzzy random variable in a population. For this purpose, we will make use of a generalized metric for fuzzy numbers, and we will develop an approach for normal fuzzy random variables, and two different approaches for the case of fuzzy random variables taking on a finite number of different values. A real-life example illustrates the use of the last two approaches. Finally, a comparison between the introduced techniques is developed by means of simulation studies leading to close inferential conclusions.

This is a preview of subscription content, access via your institution.

Author information

Affiliations

Authors

Corresponding author

Correspondence to María Ángeles Gil.

Additional information

Acknowledgements. The research in this paper has been partially supported by MCYT Grants BFM2002-01057 and BFM2001-3494. Their financial support is gratefully acknowledged. The authors are sincerely grateful to their colleague Gil González-Rodríguez for all his comments and suggestions in connection with this paper; his scientific support has been very valuable. The authors want also thank the referees of the first version of the paper because of their useful hints to improve it.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Montenegro, M., Colubi, A., Rosa Casals, M. et al. Asymptotic and Bootstrap techniques for testing the expected value of a fuzzy random variable. Metrika 59, 31–49 (2004). https://doi.org/10.1007/s001840300270

Download citation

  • Boot strap
  • Distance between fuzzy numbers
  • Fuzzy random variables
  • Large Smaple Theory