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Fuzzy Rank Linear Regression Model

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Book cover AI 2009: Advances in Artificial Intelligence (AI 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5866))

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Abstract

In this paper, we construct a fuzzy rank linear regression model using the rank transform (RT) method and least absolute deviation (LAD) method based on the α-level sets of fuzzy numbers. The rank transform method is known to be efficient when the error distribution does not satisfy the conditions for normality and the method is not sensitive to outliers in the regression analysis. Some examples are given to compare the effectiveness of the proposed method with other existing methods.

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Author information

Authors and Affiliations

  1. School of Economics, Yonsei University, Seoul, 120-749, South Korea

    Jin Hee Yoon

  2. School of Liberal Arts and Sciences, Korea Aerospace University, Koyang, 412-791, South Korea

    Seung Hoe Choi

Authors
  1. Jin Hee Yoon
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  2. Seung Hoe Choi
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Editor information

Editors and Affiliations

  1. Clayton School of Information Technology, Monash University, 3800, Clayton, VIC, Australia

    Ann Nicholson

  2. School of Computer Science and Information Technology, RMIT University, 3001, Melbourne, VIC, Australia

    Xiaodong Li

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© 2009 Springer-Verlag Berlin Heidelberg

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Yoon, J.H., Choi, S.H. (2009). Fuzzy Rank Linear Regression Model. In: Nicholson, A., Li, X. (eds) AI 2009: Advances in Artificial Intelligence. AI 2009. Lecture Notes in Computer Science(), vol 5866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10439-8_62

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  • DOI: https://doi.org/10.1007/978-3-642-10439-8_62

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10438-1

  • Online ISBN: 978-3-642-10439-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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