The paper discusses the asymptotic validity of posterior inference of pseudo‐Bayesian
quantile regression methods with complete or censored data when an asymmetric Laplace …

We consider quantile multiple regression through conditional quantile models, ie each
quantile is modeled separately. We work in the context of spatially referenced data and …

We introduce a semi-parametric Bayesian framework for a simultaneous analysis of linear
quantile regression models. A simultaneous analysis is essential to attain the true potential …

In spite of the recent surge of interest in quantile regression, joint estimation of linear
quantile planes remains a great challenge in statistics and econometrics. We propose a …

We propose an iterative algorithm to select the smoothing parameters in additive quantile
regression, wherein the functional forms of the covariate effects are unspecified and …

ABSTRACT A two-stage approach is proposed to overcome the problem in quantile
regression, where separately fitted curves for several quantiles may cross. The standard …

Characterising the joint structure of extremes of environmental variables is important for
improved understanding of those environments. Yet, many applications of multivariate …

The study of long-term trends in tide gauge data is important for understanding the present
and future risk of changes in sea-level variability for coastal zones, particularly with respect …

The occurrence and distribution of macrozoobenthos in estuaries are strongly related to
sediment grain-size characteristics. However, statistical prediction of the distribution of …

The estimation of population parameters using complex survey data requires careful
statistical modelling to account for the design features. This is further complicated by unit …