Gaussian Kernel Robust Regression (GKRReg)

Implements the Gaussian Kernel Robust Regression (GKRReg / GKRR) method proposed by De Carvalho, Lima Neto and Ferreira (2017) . The method re-weights observations iteratively using the Gaussian kernel so that poorly-fitted observations (outliers, leverage points) receive small weights, yielding resistance to Y-space outliers, X-space outliers and leverage points. Convergence is guaranteed by Propositions 4.1 and 4.2 of the original paper. Four estimators for the kernel width hyper-parameter are provided (S1: Caputo, S2: pairwise median, S3: residual variance, S4: corrected Akaike bandwidth), together with a data-driven selection rule. Inference is provided by a sandwich variance estimator with a family of heteroskedasticity-consistent finite-sample corrections adapted to the weighted leverage of the method, or by a pairs bootstrap that re-estimates the kernel width on each replicate (percentile, normal and bias-corrected accelerated intervals with p-values). Six real datasets from the robust regression literature are included to facilitate reproducible comparisons.