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RMARS: Robustification of multivariate adaptive regression spline under polyhedral uncertainty
Date
2014-03-15
Author
Ozmen, Ayse
Weber, Gerhard Wilhelm
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Since, with increased volatility and further uncertainties, financial crises translated a high "noise" within data from financial markets and economies into the related models, recent years' events in the financial world have led to radically untrustworthy representations of the future. Hence, robustification started to attract more attention in finance. The presence of noise and data uncertainty raises critical problems to be dealt with on the theoretical and computational side. For immunizing against parametric uncertainties, robust optimization has gained greatly in importance as a modeling framework from both a theoretical and a practical point of view. Consequently, we include the existence of uncertainty considering future scenarios in the multivariate adaptive regression spline (MARS) that has an apparent success in modeling real-life data in a variety of application fields, and robustify it through robust optimization proposed to cope with data and resulting model parameter uncertainty. We represent the new Robust MARS (RMARS) in theory and method and apply RMARS on financial market data. We demonstrate its good performance with a simulation study and a numerical experience that refers to basic economic indicators. Results indicate that models from RMARS have much less variability in parameter estimates and in accuracy measures, to the cost of just a slightly lower accuracy than MARS.
Subject Keywords
Applied Mathematics
,
Computational Mathematics
URI
https://hdl.handle.net/11511/56444
Journal
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
DOI
https://doi.org/10.1016/j.cam.2013.09.055
Collections
Graduate School of Applied Mathematics, Article
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BibTeX
A. Ozmen and G. W. Weber, “RMARS: Robustification of multivariate adaptive regression spline under polyhedral uncertainty,”
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
, pp. 914–924, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/56444.