Bayesian quantile regression and statistical modelling represent a growing paradigm in contemporary data analysis, extending conventional regression by estimating various conditional quantiles rather ...

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 ...

In this paper we propose a semiparametric quantile regression model for censored survival data. Quantile regression permits covariates to affect survival differently at different stages in the ...

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The human impacts of changes in heat events depend on changes in the joint behavior of temperature and humidity. Little is currently known about these complex joint changes, either in observations or ...