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Since quantile regression estimator at a fixed quantile level mainly relies on a small subset of the observed data, efforts have been made to construct simultaneous estimation at multiple quantile levels in order to take full advantage of all the observations and improve estimation efficiency. We propose a novel approach that links multiple linear quantile models through imposing a condition on the rank of the matrix formed by all the regression parameters. The approach has the flavor of thedoi:10.5705/ss.202016.0426 fatcat:ysf7lr434vh7bb7pcvjbajykgq