Transformed central quantile subspace

Quantile regression (QR) is a well-established method of tail analysis. Application QR can become very challenging when dealing with high-dimensional data, thus requiring dimension reduction techniques. While the current literature on these techniques focuses extracting linear combinations predictor variables that contain all information about conditional quantile, non-linear features potentially achieve greater reduction. We, therefore, present first application transformed for quantiles, which serves as an intermediate step between and nonlinear The idea to transform predictors monotonically then look low-dimensional projections by applying performance proposed methodology demonstrated through simulation examples real data application.