Five Things You Should Know About Quantile Regression

Paper SAS525-2017 Five Things You Should Know About Quantile RegressionRobert N. Rodriguez and Yonggang Yao, SAS Institute increasing complexity of data in research and business analytics requires versatile, robust, and scalable methods of building explanatory and predictive statistical models. Quantile Regression meets these requirements by fitting conditional quantiles of the response with a general linear model that assumes no parametric form for the conditional distribution of the response; it gives you information that you would not obtain directly from standard Regression methods. Quantile Regression yields valuable insights in applications such as risk management, where answers to important questions lie in modeling the tails of the conditional distribution.

For each quantile level ˝, the solution to the minimization problem yields a distinct set of regression coefficients. Note that ˝D0:5corresponds to median regression and 2ˆ 0:5.r/is the absolute value function.

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  Problem, Minimization, Minimization problem

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