P. López López, J. S. Verkade, A. H. Weerts, and D. P. Solomatine UNESCO – IHE Institute for Water Education, Delft, the Netherlands Deltares, Delft, the Netherlands Delft University of Technology, Delft, the Netherlands Ministry of Infrastructure and the Environment, Water Management Centre of the Netherlands, River Forecasting Service, Lelystad, the Netherlands Wageningen University and Resea...

Cardiometabolic diseases have substantially increased in China in the past 20 years and blood pressure is a primary modifiable risk factor. Using data from the China Health and Nutrition Survey we examine blood pressure trends in China from 1991 to 2009, with a concentration on age cohorts and urbanicity. Very large values of blood pressure are of interest, so we model the conditional quantile ...

We develop an abstract notion of regression which allows for a non-parametric formulation of unbiasedness. We prove then that least quantile regression is unbiased in this sense even in the heteroscedastic case if the error distribution has a continuous, symmetric, and uni-modal density. An example shows that unbiasedness may break down even for smooth and symmetric but not uni-modal error dist...

The linear quantile regression estimator is very popular and widely used. It is also well known that this estimator can be very sensitive to outliers in the explanatory variables. In order to overcome this disadvantage, the usage of the least trimmed quantile regression estimator is proposed to estimate the unknown parameters in a robust way. As a prominent measure of robustness, the breakdown ...

The Box-Cox quantile regression model introduced by Powell (1991) is a flexible and numerically attractive extension of linear quantile regression techniques. Chamberlain (1994) and Buchinsky (1995) suggest a two stage estimator for this model but the objective function in stage two of their method may not be defined in an application. We suggest a modification of the estimator which is easy to...

We study estimation in quantile regression when covariates are measured with errors. Existing methods require stringent assumptions, such as spherically symmetric joint distribution of the regression and measurement error variables, or linearity of all quantile functions, which restrict model flexibility and complicate computation. In this paper, we develop a new estimation approach based on co...

A Bayesian approach is proposed for coefficient estimation in Tobit quantile regression model. The proposed approach is based on placing a g-prior distribution depends on the quantile level on the regression coefficients. The prior is generalized by introducing a ridge parameter to address important challenges that may arise with censored data, such as multicollinearity and overfitting problems...

The work of three leading "gures in the early history of econometrics is used to motivate some recent developments in the theory and application of quantile regression. We stress not only the robustness advantages of this form of semiparametric statistical method, but also the opportunity to recover a more complete description of the statistical relationship between variables. A recent proposal...

The paper discusses the asymptotic validity of posterior inference of pseudo-Bayesian quantile regression methods with complete or censored data when an asymmetric Laplace likelihood is used. The asymmetric Laplace likelihood has a special place in the Bayesian quantile regression framework because the usual quantile regression estimator can be derived as the maximum likelihood estimator under ...

We propose Bayesian nonparametric methodology for quantile regression modeling. In particular, we develop Dirichlet process mixture models for the error distribution in an additive quantile regression formulation. The proposed nonparametric prior probability models allow the data to drive the shape of the error density and thus provide more reliable predictive inference than models based on par...