Background: Poverty and low socioeconomic status are the most important reasons of increasing the global burden of tuberculosis, not only in developing countries but also in developed countries for particular groups. The purpose of this study was to assess the association between socioeconomic factors and the number of tuberculosis patients using quantile regression for count data.   Me...

Conditional quantile estimation is an essential ingredient in modern risk management. Although GARCH processes have proven highly successful in modeling financial data it is generally recognized that it would be useful to consider a broader class of processes capable of representing more flexibly both asymmetry and tail behavior of conditional returns distributions. In this paper, we study esti...

Quantile regression is an increasingly important empirical tool in economics and other sciences for analyzing the impact of a set of regressors on the conditional distribution of an outcome. Extremal quantile regression, or quantile regression applied to the tails, is of interest in many economic and financial applications, such as conditional value-at-risk, production efficiency, and adjustmen...

We propose a new adaptive L1 penalized quantile regression estimator for highdimensional sparse regression models with heterogeneous error sequences. We show that under weaker conditions compared with alternative procedures, the adaptive L1 quantile regression selects the true underlying model with probability converging to one, and the unique estimates of nonzero coefficients it provides have ...

Regression quantiles, along with the dual methods of regression rank scores, can be considered one of the major statistical breakthroughs of the past decades. Its advantages over the other estimation methods have been well investigated. Regression quantile methods provide a much more complete statistical analysis of the stochastic relationships among variables; in addition, they are more robust...

Estimation of reference growth curves for children's height and weight has traditionally relied on normal theory to construct families of quantile curves based on samples from the reference population. Age-specific parametric transformation has been used to significantly broaden the applicability of these normal theory methods. Non-parametric quantile regression methods offer a complementary st...

[To be revised.] Quantile and expectile regression are tail oriented conditional regression. They can be transformed as generalized quantile regression. Traditional generalized quantile regression focuses on a single curve. When more random curves are available, we can estimate the single curves jointly by using the information from all subjects instead of estimate it individually. To avoid too...