A spatial semiparametric M-quantile regression for hedonic price modelling

Bayesian semiparametric additive quantile regression

Quantile regression provides a convenient framework for analyzing the impact of covariates on the complete conditional distribution of a response variable instead of only the mean. While frequentist treatments of quantile regression are typically completely nonparametric, a Bayesian formulation relies on assuming the asymmetric Laplace distribution as auxiliary error distribution that yields po...

www.econstor.eu Bayesian semiparametric additive quantile regression

Semiparametric Quantile Regression with High-dimensional Covariates.

This paper is concerned with quantile regression for a semiparametric regression model, in which both the conditional mean and conditional variance function of the response given the covariates admit a single-index structure. This semiparametric regression model enables us to reduce the dimension of the covariates and simultaneously retains the flexibility of nonparametric regression. Under mil...

Efficient Semiparametric Seemingly Unrelated Quantile Regression Estimation

We propose an efficient semiparametric estimator for the coefficients of a multivariate linear regression model — with a conditional quantile restriction for each equation — in which the conditional distributions of errors given regressors are unknown. The procedure can be used to estimate multiple conditional quantiles of the same regression relationship. The proposed estimator is asymptotical...

Bayesian Spatial Quantile Regression

Statistical Science) Bayesian Spatial Quantile Regression by Kristian Lum Department of Statistical Science Duke University