Abstract
The inferential results regarding estimates of Support Vector Regression (SVR) are highly influenced by anomalies and ill-conditioned predictors. Excessive dimensions of data also make the model complex. To improve estimation accuracy, this paper introduces two modelling frameworks, Principal Component Robust Support Vector Regression (PCRSVR) and Principal Fitted Component Robust Support Vector Regression (PFCRSVR). These techniques are developed by incorporating PCs and PFCs with Exponential Quantile SVR (EQSVR), which is capable of dealing with ill-conditioned regressors, extreme observations, and high-dimensional data settings simultaneously. An extensive simulation study has been conducted to evaluate the performance of the proposed methods. Different evaluation criteria are chosen in this regard. Additionally, real-life data applications illustrate the efficacy of the proposed techniques as compared to competing ones.