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Quantile regression as an alternative to conditional mean regression (i.e., least square regression) is widely used in many areas. It can be used to study the covariate effects on the entire response distribution by fitting quantile regression models at multiple different quantiles or even fitting the entire regression quantile process. However, estimating the regression quantile process is inherently difficult because the induced conditional quantile function needs to be monotone at alldoi:10.1080/00401706.2016.1211553 fatcat:3wmlb5yqvjf7zlcwte3ccrrvmq