Bayesian quantile regression method based on fusion Lasso penalty variables
In order to solve the difficulty of parameter estimation and variable selection caused by the presence of a large number of unknown random and fixed effects in mixed effects quantile regression models,a Bayesian mixed effects quantile regression method with fused Lasso penalty is proposed to estimate coefficients,the posterior distribution of the model is given,and a Gibbs sampling algorithm for parameter estimation is constructed.Simulation results show that this method has strong robustness under different random error distributions,and performs better on sparse data types com-pared to dense data types.In variable selection problems,it can not only select important variables,but also push irrelevant variables towards zero,which improves the model's fatigue and interpretability,and provides an effective modeling method for practical researchers studying sparse longitudinal data.
万方数据
维普
