: At its core, estimating quantiles is an optimization problem. The book provides a "softened" mathematical journey into linear programming, using the simplex algorithm and two-phase methods to solve the quantile regression framework.
Quantile regression is uniquely powerful for identifying "limiting constraints" on populations. For example, in public health, it can reveal if a treatment benefits those at the highest risk (the upper quantiles) differently than those at lower risk, providing a complete "statistical landscape" rather than just a single point of view. Practical Implementation Quantile Regression Volume 2 Estimation and Sim...
: It moves beyond standard quantiles to compare them with expectiles, M-estimators, and M-quantiles , offering a more robust toolkit for dealing with outliers and non-normal distributions. : At its core, estimating quantiles is an
While the first volume introduced the basics, Volume 2 tackles the technical machinery required for complex, real-world data analysis: For example, in public health, it can reveal
Standard linear regression tells us about the "average" effect, but what about the extremes? In the second volume of ( Wiley , 2018), authors Marilena Furno and Domenico Vistocco provide a practical roadmap for researchers to move beyond the mean and explore the entire conditional distribution. Key Pillars of Volume 2