ABSTRACT: Variable selection using penalized estimation methods in quantile regression models is an important step in screening for relevant covariates. In this paper, we present a one-step estimation ...

Birth weight (BW) is a key indicator of a newborn’s health, survival, and development. It is associated with the risk of childhood mortality and is also related to health, physical growth, emotional ...

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 ...

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 ...

The quantile varying coefficient model is robust to data heterogeneity, outliers and heavy-tailed distributions in the response variable. In addition, it can flexibly model dynamic patterns of ...

Abstract: The battery thermal management of electric vehicles can be improved using neural networks predicting quantile sequences of the battery temperature. This work extends a method for the ...

Abstract: Quantile regression model estimates the relationship between covariates and the quantile of a response distribution, rather than the mean. This method has been utilized successfully in ...

ABSTRACT: The selection of predictors plays a crucial role in building a multiple regression model. Indeed, the choice of a suitable subset of predictors can help to improve prediction accuracy and ...