This is a preview. Log in through your library . Abstract Using RUMiC data and a simple panel quantile regression method, this paper accounts for the time-invariant individual specific characteristics ...

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 ...

One of the more difficult challenges for modeling is deciding how (or if) to deal with extreme data points. It’s a common problem in economic and financial numbers. Fat tailed distributions are ...

We study quantile regression (QR) for longitudinal measurements with nonignorable intermittent missing data and dropout. Compared to conventional mean regression, quantile regression can characterize ...

The goal of a machine learning regression problem is to predict a single numeric value. Quantile regression is a variation where you are concerned with under-prediction or over-prediction. I'll phrase ...

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 ...