SavedAdd your journal

Journals / Mathematical and Computational Applications / 2012 / Cilt: 17 - Sayı: 3

A study on fuzzy robust regression and its application to insurance

KAMİLE ŞANLI KULA
FATİH TANK
Türkan ERBAY DALKILIÇ
Pages
223–234
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Fuzzy robust regression is finally investigated and discussed by an example with real data arose from a well-known insurance company in Turkey.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, a fuzzy robust regression method is proposed to construct a model that describes the relation between dependent and independent variables in insurance.
📊
Key Findings & Data:Fuzzy robust regression suggested as an alternative to not only ordinary least squares but also classical robust regression.
💡
Conclusion & Impact:Fuzzy robust regression is finally investigated and discussed by an example with real data arose from a well-known insurance company in Turkey.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In this study, a fuzzy robust regression method is proposed to construct a model that describes the relation between dependent and independent variables in insurance. Fuzzy robust regression suggested as an alternative to not only ordinary least squares but also classical robust regression. Fuzzy robust regression is finally investigated and discussed by an example with real data arose from a well-known insurance company in Turkey.

🔖

Cite This Article

KULA, K. Ş., TANK, F., DALKILIÇ, T. E. (2012). A study on fuzzy robust regression and its application to insurance. Mathematical and Computational Applications, Cilt: 17 - Sayı: 3, 223–234. https://ajindex.com/articles/280306

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (20)

  1. W.D. Berry, S. Feldman, Multiple Regression in Practice, Sage University Papers Series, Quantitative Applications in the Social Sciences, No. 07-050, 1985
  2. P.T. Chang, and E.S. Lee, A generalized fuzzy weighted least-squares regression, Fuzzy Sets and Systems 82: 289-298, 1996
  3. Y.H.O. Chang, Hybrid fuzzy least-squares regression analysis and its reliability measures. Fuzzy Sets and Systems 119: 225-246, 2001
  4. T.E. Dalkilic, F. Tank, and K.S. Kula, Neural networks approach for determining total claim amounts in insurance, Insurance: Mathematics and Economics 45: 236-241, 2009
  5. R.N. Dave, and R. Krishnapuram, Robust clustering methods: A unified view, IEEE Transactions on Fuzzy Systems 5: 270-293, 1997
  6. P. Diamond, Fuzzy least squares, Information Science 46: 141-157, 1988
  7. J. Fox, An R and S-PLUS Companion to Applied Regression, Sage Publications, 2002
  8. D. N. Gujarati, Essentials of Econometrics, St. Louis, MO: McGraw-Hill, Inc., 1992
  9. F.R., Hampel, E.M., Ronchetti, Rousseeuw, P.J. and Stahel, W.A, Robust statistics, John Willey & Sons, New-York, 1986
  10. P.J. Huber, Robust Estimation of a Location Parameter, Annals of Mathematical Statistics 35: 73-101, 1964
  11. P.J. Huber, Robust Statistics, John Willey & Son, New York (1981)
  12. W. L. Hung and M. S. Yang, An omission approach for detecting outliers in fuzzy regression models, Fuzzy Sets and Systems 157: 3109-3122 (2006)
  13. H. Huynh, A comparison of four approaches to robust regression, Psychological Bulletin 92: 505-512 (1982)
  14. R.V. Hogg, Statistical robustness: one view of its use in applications today, The American Statistican 33: 108-115 (1979)
  15. K.S., Kula, A. Apaydin, Fuzzy Robust Regression Analaysis Based on the Ranking of Fuzzy Sets, Int. J. Uncertainty, Fuzziness and Knowledge-Based Systems (IJUFKS) 16: 663-681 (2008)
  16. M.M. Nasrabadi and E. Nasrabadi, A Mathematical-programming approach to fuzzy linear regression analysis, Applied Mathematics and Computation 163: 977-989 (2004)
  17. E. Nasrabadi, S. M. Hashemi and M. Ghatee, An LP approach to outliers detection in fuzzy regression analysis, Int. J. Uncertainty, Fuzziness and Knowledge-Based Systems (IJUFKS) 15: 441-456 (2007)
  18. D.T. Redden and W.H. Woddall, Futher examination of fuzzy linear regression, Fuzzy Sets and Systems 79: 203-211 (1996)
  19. P. Rousseeuw, B. Daniels, A.Leroy, Applying robust regression to insurance, Insurance: Mathematics and Economics 3: 67-72 (1984)
  20. P.J. Rousseuw, and