A1125
Title: On interquantile smoothness of censored quantile regression with induced smoothing
Authors: Tony Sit - The Chinese University of Hong Kong (Hong Kong) [presenting]
Abstract: Quantile regression has emerged as a useful and effective tool in modelling survival data, especially for cases where noises demonstrate heterogeneity. Despite recent advancements, non-smooth components involved in censored quantile regression estimators may often yield numerically unstable results, which, in turn, lead to potentially self-contradicting conclusions. An estimating equation-based approach is proposed to obtain consistent estimators of the regression coefficients of interest via the induced smoothing technique to circumvent the difficulty. The proposed estimator can be shown to be asymptotically equivalent to its original unsmoothed version, whose consistency and asymptotic normality can be readily established. Extensions to handle functional covariate data and recurrent event data are also discussed. To alleviate the heavy computational burden of bootstrap-based variance estimation, an efficient resampling procedure is also proposed that reduces the computational time considerably. The numerical studies demonstrate that the proposed estimator provides substantially smoother model parameter estimates across different quantile levels and can achieve better statistical efficiency than a plain estimator under various finite-sample settings. The proposed method is also illustrated via four survival datasets, including the HMO HIV data, the primary biliary cirrhosis (PBC) data, etc.
