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Strong consistency of least-squares estimation in linear regression models with vague concepts

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  • Krätschmer, Volker

Abstract

Linear regression models with vague concepts extend the classical single equation linear regression models by admitting observations in form of fuzzy subsets instead of real numbers. They have recently been introduced [cf. Krätschmer, Induktive statistik auf basis unscharfer meßkonzepte am beispiel linearer regressionsmodelle, Unpublished Habilitation Monograph, Faculty of Law and Economics of the University of Saarland, Saarbrücken, 2001] to improve the empirical meaningfulness of the relationship between the involved items by a more sensitive attention to the problems of data measurement, in particular the fundamental problem of adequacy. The parameters of such models are still real numbers, and a method of estimation can be applied which extends directly the ordinary least-squares method. This paper deals with some first asymptotic properties of estimators obtained by the method. Firstly, strong consistency will be shown, and secondly, the convergence rate will be investigated. The later result will be the starting point for a future study which will calculate the limit distributions of the estimators.

Suggested Citation

  • Krätschmer, Volker, 2006. "Strong consistency of least-squares estimation in linear regression models with vague concepts," Journal of Multivariate Analysis, Elsevier, vol. 97(3), pages 633-654, March.
    • Handle: RePEc:eee:jmvana:v:97:y:2006:i:3:p:633-654
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    Cited by:

    1. Lopez-Diaz, Miguel & Ralescu, Dan A., 2006. "Tools for fuzzy random variables: Embeddings and measurabilities," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 109-114, November.
    2. Giordani, Paolo, 2010. "Three-way analysis of imprecise data," Journal of Multivariate Analysis, Elsevier, vol. 101(3), pages 568-582, March.
    3. Coppi, Renato & Gil, Maria A. & Kiers, Henk A.L., 2006. "The fuzzy approach to statistical analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 1-14, November.

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