type="main" xml:id="stan12035-abs-0001"We propose composite quantile regression for dependent data, in which the errors are from short-range dependent and strictly stationary linear processes. Under some regularity conditions, we show that composite quantile estimator enjoys root-n consistency...

In this paper, we develop a new variable selection procedure for quantile varying coefficient models with longitudinal data. The proposed method is based on basis function approximation and a class of group versions of the adaptive LASSO penalty, which penalizes the Lγ norm of the within-group...

We develop a method for sequential detection of structural changes in linear quantile regression models. We establish the asymptotic properties of the proposed test statistic, and demonstrate the advantages of the proposed method over existing tests through simulation.

Quantile regression in the presence of fixed censoring has been studied extensively in the literature. However, existing methods either suffer from computational instability or require complex procedures involving trimming and smoothing, which complicates the asymptotic theory of the resulting...

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...

In modelling volatility in financial time series, the double‐threshold autoregressive conditional heteroscedastic (DTARCH) model has been demonstrated as a useful variant of the autoregressive conditional heteroscedastic (ARCH) models. In this paper, we propose a weighted composite quantile...