Bayesian quantile regression methods
研究贝叶斯指数倾斜经验似然在分位数回归推断中的应用，推导了简单分位数和回归分位数的后验密度形式，并检验了马尔可夫链蒙特卡洛模拟算法在内生变量模型中的表现。
Abstract This paper is a study of the application of Bayesian exponentially tilted empirical likelihood to inference about quantile regressions. In the case of simple quantiles we show the exact form for the likelihood implied by this method and compare it with the Bayesian bootstrap and with Jeffreys' method. For regression quantiles we derive the asymptotic form of the posterior density. We also examine Markov chain Monte Carlo simulations with a proposal density formed from an overdispersed version of the limiting normal density. We show that the algorithm works well even in models with an endogenous regressor when the instruments are not too weak. Copyright © 2009 John Wiley & Sons, Ltd.