Bayesian inference provides a flexible way of combining data with prior information. However, quantile regression is not equipped with a parametric likelihood, and therefore, Bayesian inference for ...

Over the past decades, researchers have refined fuzzy regression techniques to incorporate advanced statistical methods such as Bayesian inference, kernel-based smoothing, and nonlinear quantile ...

Additionally, additive quantile mixed-effects models have been developed to elucidate nonlinear relationships in patient data, effectively mapping the progression of clinical markers over time [4].

A Bayesian method for outlier-robust estimation of multinomial choice models is presented. The method can be used for both correlated as well as uncorrelated choice alternatives and guarantees ...