Bayesian Nonlinear Quantile Regression Approach For Ordinal Longitudinal Data

In this article,we propose a Bayesian approach to estimate the parameters of the nonlinear quantile regression model for ordinal longitudinal data.Firstly,we use a con-tinuous latent variable to deal with ordinal response.Then we establish a nonlinear quantile regression model.By utilizing the relation between the objective function and the density function of the asymmetric Laplace distribution,we convert the peoblem to get the maximum likelihood estimation.Next,we carry out the Bayesian method.Be-cause the difficulty in computing the posterior distribution,the Markov Chains Monte Carlo method is used.In this article,we conducted two simulation studies to assess the performance of our method,and then apply our method to the real data analysis,and the results show that our method performs well.