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J. Tezcan¹, Y. Dak Hazirbaba¹ and Q. Cheng²

¹Department of Civil and Environmental Engineering, Southern Illinois University Carbondale, United States
²Computer Science Department, Southern Illinois University Carbondale, United States

doi:10.4203/ccp.106.47

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J. Tezcan, Y. Dak Hazirbaba, Q. Cheng, "Least-Squares-Kernel-Machine Regression for Earthquake Ground Motion Prediction", in , (Editors), "Proceedings of the Twelfth International Conference on Computational Structures Technology", Civil-Comp Press, Stirlingshire, UK, Paper 47, 2014. doi:10.4203/ccp.106.47

Keywords: least-square-kernel-machine, mixed effect model, semi-parametric regression, residual maximum likelihood method, ground motion analysis, covariance matrix..

Summary

This paper presents a semi-parametric mixed-effect regression approach for analysing and modelling earthquake ground motions, taking into account the correlations between records. Using kernels, the proposed method extends the classical mixed model equations to complicated relationships. The predictive equation is composed of parametric and nonparametric parts. The parametric part incorporates known relationships into the model, while the nonparametric part captures the relationships which cannot be cast into a simple parametric form. A least squares kernel machine is used to infer the nonparametric part of the model. The resulting semi-parametric model combines the strengths of parametric and nonparametric approaches, allowing incorporation of prior, well-justified knowledge into the model while retaining flexibility with respect to the explanatory variables for which the functional form is uncertain. The validity of the proposed method is demonstrated through an example.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 106 PROCEEDINGS OF THE TWELFTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STRUCTURES TECHNOLOGY Edited by: Paper 47 Least-Squares-Kernel-Machine Regression for Earthquake Ground Motion Prediction J. Tezcan¹, Y. Dak Hazirbaba¹ and Q. Cheng² ¹Department of Civil and Environmental Engineering, Southern Illinois University Carbondale, United States ²Computer Science Department, Southern Illinois University Carbondale, United States doi:10.4203/ccp.106.47 purchase the full-text of this paper Full Bibliographic Reference for this paper J. Tezcan, Y. Dak Hazirbaba, Q. Cheng, "Least-Squares-Kernel-Machine Regression for Earthquake Ground Motion Prediction", in , (Editors), "Proceedings of the Twelfth International Conference on Computational Structures Technology", Civil-Comp Press, Stirlingshire, UK, Paper 47, 2014. doi:10.4203/ccp.106.47 Keywords: least-square-kernel-machine, mixed effect model, semi-parametric regression, residual maximum likelihood method, ground motion analysis, covariance matrix.. Summary This paper presents a semi-parametric mixed-effect regression approach for analysing and modelling earthquake ground motions, taking into account the correlations between records. Using kernels, the proposed method extends the classical mixed model equations to complicated relationships. The predictive equation is composed of parametric and nonparametric parts. The parametric part incorporates known relationships into the model, while the nonparametric part captures the relationships which cannot be cast into a simple parametric form. A least squares kernel machine is used to infer the nonparametric part of the model. The resulting semi-parametric model combines the strengths of parametric and nonparametric approaches, allowing incorporation of prior, well-justified knowledge into the model while retaining flexibility with respect to the explanatory variables for which the functional form is uncertain. The validity of the proposed method is demonstrated through an example. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £65 +P&P)
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