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We propose a new test for covariate-threshold caused change point in quantile regression with random censoring, based on partial subgradient. Critical values are obtained using wild bootstrap samples, where induced smoothing method is used to estimate the conditional density. [ABSTRACT FROM AUTHOR]

A weighted approach is developed to improve estimation efficiency in varying-coefficient quantile regression model, with longitudinal data. The weights are obtained from empirical likelihood of varying-coefficient mean model, where the nonparametric functions are approximated by basis splines, and the matrix expansion idea in quadratic inference function method is used, to model the inverse of conditional correlation matrix within subject. Theoretical results show that, the weighted estimators of the varying coefficients in quantile regression, can achieve higher efficiency than conventional estimators without weighting scheme. Simulation studies are used to assess the finite sample performance and a real data analysis is also conducted. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]