Quantile regression (QR) has received increasing attention in recent years and applied to
wide areas such as investment, finance, economics, medicine and engineering. Compared …

Alle im vorigen Kapitel beschriebenen Problemstellungen besitzen eine wesentliche
Gemeinsamkeit: Eigenschaften einer Zielvariablen y sollen in Abhängigkeit von Kovariablen …

Dependent data arise in many studies. Frequently adopted sampling designs, such as
cluster, multilevel, spatial, and repeated measures, may induce this dependence, which the …

Inference in quantile analysis has received considerable attention in the recent years. Linear
quantile mixed models (Geraci and Bottai 2014) represent a flexible statistical tool to …

We provide an overview of linear quantile regression models for continuous responses
repeatedly measured over time. We distinguish between marginal approaches, that explicitly …

Tropospheric ozone is one of the six criteria pollutants regulated by the United States
Environmental Protection Agency under the Clean Air Act and has been linked with several …

Regularization, eg lasso, has been shown to be effective in quantile regression in improving
the prediction accuracy (Li and Zhu, 2008; Wu and Liu, 2009). This paper studies …

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

Usual exponential family regression models focus on only one designated quantity of the
response distribution, namely the mean. While this entails easy interpretation of the …

Bayesian empirical likelihood for quantile regression Page 1 The Annals of Statistics 2012, Vol.
40, No. 2, 1102–1131 DOI: 10.1214/12-AOS1005 © Institute of Mathematical Statistics, 2012 …