Estimating the conditional quantiles of outcome variables of interest is frequent in many research areas, and quantile regression is foremost among the utilized methods. The coefficients of a quantile ...

We address the Least Quantile of Squares (LQS) (and in particular the Least Median of Squares) regression problem using modern optimization methods. We propose a Mixed Integer Optimization (MIO) ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of neural network quantile regression. The goal of a quantile regression problem is to predict a single numeric ...

Beside the model, the other input into a regression analysis is some relevant sample data, consisting of the observed values of the dependent and explanatory variables for a sample of members of the ...

Quantile regression techniques were used to estimate the influence of employment and hours worked on percentage weight change over 2 years across the entire distribution of weight change in a cohort ...

Financial supporters for this research include Senior Fellowships of KU Leuven, University of Piraeus Research Center. Our news journalists obtained a quote from the research from the Catholic ...