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

Download

index.pdf

Date

2016-01-01

Author

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

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

183
views

145
downloads

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

Suggestions

OpenMETU
Core

A Hybrid Computational Method based on Convex Optimizationfor Outlier Problems Yerlikaya Ozkurt, Fatma; Askan Gündoğan, Ayşegül; Weber, Gerhard Wiehelm (2015-11-01) 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.
An algorithm to analyze stability of gene-expression patterns Gebert, J; Latsch, M; Pickl, SW; Weber, Gerhard Wilhelm; Wunschiers, R (Elsevier BV, 2006-05-01) Many problems in the field of computational biology consist of the analysis of so-called gene-expression data. The successful application of approximation and optimization techniques, dynamical systems, algorithms and the utilization of the underlying combinatorial structures lead to a better understanding in that field. For the concrete example of gene-expression data we extend an algorithm, which exploits discrete information. This is lying in extremal points of polyhedra, which grow step by step, up to a...
A metamodeling methodology involving both qualitative and quantitative input factors Tunali, S; Batmaz, I (Elsevier BV, 2003-10-16) This paper suggests a methodology for developing a simulation metamodel involving both quantitative and qualitative factors. The methodology mainly deals with various strategic issues involved in metamodel estimation, analysis, comparison, and validation. To illustrate how to apply the methodology, a regression metamodel is developed for a client-server computer system. In particular, we studied how the response time is affected by the quantum interval, the buffer size. and the total number of terminals whe...
A pattern classification approach for boosting with genetic algorithms Yalabık, Ismet; Yarman Vural, Fatoş Tunay; Üçoluk, Göktürk; Şehitoğlu, Onur Tolga (2007-11-09) Ensemble learning is a multiple-classifier machine learning approach which produces collections and ensembles statistical classifiers to build up more accurate classifier than the individual classifiers. Bagging, boosting and voting methods are the basic examples of ensemble learning. In this study, a novel boosting technique targeting to solve partial problems of AdaBoost, a well-known boosting algorithm, is proposed. The proposed system finds an elegant way of boosting a bunch of classifiers successively ...
A multicriteria sorting approach based on data envelopment analysis for R&D project selection problem Karasakal, Esra (Elsevier BV, 2017-12-01) In this paper, multiple criteria sorting methods based on data envelopment analysis (DEA) are developed to evaluate research and development (R&D) projects. The weight intervals of the criteria are obtained from Interval Analytic Hierarchy Process and employed as the assurance region constraints of models. Based on data envelopment analysis, two threshold estimation models, and five assignment models are developed for sorting. In addition to sorting, these models also provide ranking of the projects. The de...

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.