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On Optimal and Asymptotic Properties of a Fuzzy L 2 Estimator

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  • Jin Hee Yoon

    (Department of Mathematics and Statistics, Sejong University, Seoul 05006, Korea)

  • Przemyslaw Grzegorzewski

    (Faculty of Mathematics and Information Science, Warsaw University of Technology, Koszykowa 75, 00-662 Wasaw, Poland
    Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447 Warsaw, Poland)

Abstract

A fuzzy least squares estimator in the multiple with fuzzy-input–fuzzy-output linear regression model is considered. The paper provides a formula for the L 2 estimator of the fuzzy regression model. This paper proposes several operations for fuzzy numbers and fuzzy matrices with fuzzy components and discussed some algebraic properties that are needed to use for proving theorems. Using the proposed operations, the formula for the variance, provided and this paper, proves that the estimators have several important optimal properties and asymptotic properties: they are Best Linear Unbiased Estimator (BLUE), asymptotic normality and strong consistency. The confidence regions of the coefficient parameters and the asymptotic relative efficiency (ARE) are also discussed. In addition, several examples are provided including a Monte Carlo simulation study showing the validity of the proposed theorems.

Suggested Citation

  • Jin Hee Yoon & Przemyslaw Grzegorzewski, 2020. "On Optimal and Asymptotic Properties of a Fuzzy L 2 Estimator," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:11:p:1956-:d:440138
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    References listed on IDEAS

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    1. D'Urso, Pierpaolo & Gastaldi, Tommaso, 2000. "A least-squares approach to fuzzy linear regression analysis," Computational Statistics & Data Analysis, Elsevier, vol. 34(4), pages 427-440, October.
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