Researchers have developed a gene therapy that significantly slowed motor function loss in preclinical models of amyotrophic lateral sclerosis (ALS), offering new hope for treating the devastating ...

They offer promising solutions for quantile-based analyses in decentralized sensor networks across diverse applications. It should be noted that quantile regression involves a non-differentiable ...

Learn what is Linear Regression Cost Function in Machine Learning and how it is used. Linear Regression Cost function in Machine Learning is "error" representation between actual value and model ...

Compared to mean regression, quantile regression offers greater flexibility and insight into the underlying associations, especially when modeling anthropometric measurements and nutritional status.

We build quantile regression trees by using the quantile loss function in our node splitting criterion. The performance of our algorithm stems from new online update procedures for both the quantile ...

By using a custom loss function that penalizes low predictions more than high predictions, you can coerce the network to make high predictions to a specified quantile value, such as 90th percentile.

ABSTRACT: As extremely important methods, Lp regression methods have attracted the attention of either theoretical or empirical researchers all over the world. As special cases of that, quantile and ...

Learn how to choose, apply, and improve loss functions for regression problems in artificial neural networks with outliers and noisy data.

Support vector quantile regression (SVQR) is capable of providing a good description of the linear and nonlinear relationships among random variables. In this paper we propose a sparse SVQR to ...