This paper presents a semi-parametric mixed-effect regression approach for analyzing and modeling 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. Equations for pointwise confidence and prediction intervals around the conditional mean are provided. The validity of the proposed method is demonstrated through numerical simulations and using recorded ground motions.
|Number of pages||10|
|Journal||Advances in Engineering Software|
|State||Published - Jun 2018|
Bibliographical noteFunding Information:
This material is based upon work supported by theNational Science Foundation under Grant No. CMMI 1100735 and IIS-1218712.
This material is based upon work supported by the National Science Foundation under Grant No. CMMI 1100735 and IIS-1218712 .
© 2017 Civil-Comp Ltd. and Elsevier Ltd.
- Confidence intervals
- Ground motion analysis
- Least squares kernel machine
- Mixed-effect model
- Residual maximum likelihood method
- Semi-parametric regression
ASJC Scopus subject areas
- Engineering (all)