A Hybrid Computational Method based on Convex Optimizationfor Outlier Problems

Date

2015-11-01

Author

Yerlikaya Ozkurt, Fatma
Askan Gündoğan, Ayşegül
Weber, Gerhard Wiehelm

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Statistical modeling plays a central role for any prediction problem of interest.However, predictive models may give misleading results when the data containoutliers. In many applications, it is important to identify and treat the outlierswithout direct elimination. To handle such issues, a hybrid computational methodbased on conic quadratic programming is introduced and employed onearthquake ground motion data set. Results are compared against widely-usedground motion prediction models.

URI

https://cld.bz/KAj90ao#466
https://hdl.handle.net/11511/70782

Conference Name

The Institute for Operations Research and the Management Sciences (INFORMS) 2015 Annual Meeting

Collections

Department of Civil Engineering, Conference / Seminar

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Citation Formats

F. Yerlikaya Ozkurt, A. Askan Gündoğan, and G. W. Weber, “A Hybrid Computational Method based on Convex Optimizationfor Outlier Problems,” presented at the The Institute for Operations Research and the Management Sciences (INFORMS) 2015 Annual Meeting, 2015, Accessed: 00, 2021. [Online]. Available: https://cld.bz/KAj90ao#466.