Abstract
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.