A Hybrid Computational Method Based on Convex Optimization for Outlier Problems: Application to Earthquake Ground Motion Prediction

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2016-01-01

Author

Yerlikaya-Ozkurt, Fatma
Askan Gündoğan, Ayşegül
Weber, Gerhard-Wilhelm

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Statistical modelling plays a central role for any prediction problem of interest. However, predictive models may give misleading results when the data contain outliers. In many real -world applications, it is important to identify and treat the outliers without direct elimination. To handle such issues, a hybrid computational method based on conic quadratic programming is introduced and employed on earthquake ground motion dataset. This method aims to minimize the impact of the outliers on regression estimators as well as handling the nonlinearity in the dataset. Results are compared against widely used parametric and nonparametric ground motion prediction models.

Subject Keywords

Applied Mathematics, Information Systems

URI

https://hdl.handle.net/11511/39537

Journal

INFORMATICA

DOI

https://doi.org/10.15388/informatica.2016.116

Collections

Department of Civil Engineering, Article

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Citation Formats

F. Yerlikaya-Ozkurt, A. Askan Gündoğan, and G.-W. Weber, “A Hybrid Computational Method Based on Convex Optimization for Outlier Problems: Application to Earthquake Ground Motion Prediction,” INFORMATICA, pp. 893–910, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/39537.