Bayesian Quantile Regression and Statistical Modelling Publication Trend The graph below shows the total number of publications each year in Bayesian Quantile Regression and Statistical Modelling.

As a result, running linear and quantile regression yields similar results. (Note: the QR model uses the median quantile in this example.) ...

We study censored quantile regression with covariates measured with errors. We propose a composite quantile objective function based on inverse censoring-probability weighting, and an averaging ...

To facilitate the practical implementation of these methods, we develop an iterative method based on the majorize/minimize algorithm for computing the quantile regression estimates, and a bootstrap ...

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 ...

The technique of parameter estimation in linear models using the notion of regression quantiles is a generalization of the LAE or LAV least absolute value estimation technique. For a given quantile q, ...

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 ...