For example, adaptations of the unit Burr-XII distribution for quantile regression modelling have demonstrated enhanced flexibility in accommodating varying data dispersions and asymmetric shapes [2].

We propose a penalized quantile regression and an independence screening procedure to identify important covariates and to exclude unimportant ones for a general class of ultrahigh dimensional single ...

Quantile regression has emerged as a significant extension of traditional linear models and its potential in survival applications has recently been recognized. In this paper we study quantile ...

First, we provide details of the quantile regression model for right-censored data, the inference, and the goodness of fit. Next, we illustrate the methodology with a reconstructed dataset from a ...

You can then modify the model's prediction method by adding some constant to the normal predicted values so that 80% of the predictions are greater than the associated target values. This naive ...

But in neural network quantile regression, you must also watch the current quantile value to make sure you approach the desired quantile percentile value. Evaluating and Using the Neural Network ...