They are variations of linear regression (and therefore not very powerful) and are extremely difficult to train because they require linear programming (and therefore very complex). However, a ...

One solution is to ditch the standard linear regression and replace it with quantile regression, which is less vulnerable to extreme data points. What’s the difference in these regressions?

We propose novel transformation models for singly or doubly bounded responses, which improve upon the performance of conditional quantile estimators as compared to other competing transformations, ...

Susanne M. Schénnach, Quantile Regression with Mismeasured Covariates, Econometric Theory, Vol. 24, No. 4 (Aug., 2008), pp. 1010-1043 ...

Beta regression offers a robust framework for analysing data that are confined to the unit interval, enabling researchers to model proportions, probabilities, and other fractional outcomes with ...

Scikit-learn, the popular Python-based machine learning (ML) library, has released version 1.0. Although the library has been stable for some time, and the release contains no breaking changes ...

In this paper, we suggest to use quantile regression for survival data to quantify the long-term benefit of immunotherapy. Our motivation is that this approach is not trial-specific and provides ...