Abstract:Although the variable selection and regularization procedures have been extensively considered in the literature for the quantile regression (QR) scenario via penalization, many such procedures fail to deal with data aberrations in the design space, namely, high leverage points (X-space outliers) and collinearity challenges simultaneously. Some high leverage points referred to as collinearity influential observations tend to adversely alter the eigenstructure of the design matrix by inducing or masking colline… Show more
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