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

Missing data, a common but challenging issue in most studies, may lead to biased and
inefficient inferences if handled inappropriately. As a natural and powerful way for dealing …

This research crucially investigates COVID-19 variables' impacts on the changing
distributions of travel and leisure industry returns across 65 countries via a quantile …

The study of how uncertainty affects hospitality firms' decisions is gaining attention in recent
years. We examine the effect of Economic Policy Uncertainty (EPU) on the investment policy …

Missing data affect nearly every discipline by complicating the statistical analysis of collected
data. But since the 1990s, there have been important developments in the statistical …

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

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

Recently, variable selection by penalized likelihood has attracted much research interest. In
this paper, we propose adaptive Lasso quantile regression (BALQR) from a Bayesian …

Longitudinal studies often incur several problems that challenge standard statistical
methods for data analysis. These problems include non-ignorable missing data in …

This study analyzes the impact of economic policy uncertainty (EPU) on the performance of
US tourism firms using a sample of 296 publicly traded tourism companies from 2000 to …