Five Things You Should Know About Quantile Regression - SAS

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. Furthermore, Quantile Regression is capable of modeling the entire conditional distribution; this is essential for applications such as ranking the performance of students on standardized exams.

The quantile level is the probability (or the proportion of the population) that is associated with a quantile. The quantile level is often denoted by the Greek letter ˝, and the corresponding conditional quantile of Y given X is often written as Q ˝.YjX/.The quantile level ˝is the probability Pr„Y Q ˝.Y jX/X“, and it is the value of Y below which the ...

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