A0246
Title: Distributed censored quantile regression
Authors: Tony Sit - The Chinese University of Hong Kong (Hong Kong) [presenting]
Abstract: An extension of censored quantile regression to a distributed setting is discussed. With the growing availability of massive datasets, it is oftentimes an arduous task to analyse all the data with limited computational facilities efficiently. The proposed method, which attempts to overcome this challenge, consists of two key steps, namely: (i) estimation of both Kaplan-Meier estimator and model coefficients in a parallel computing environment; (ii) aggregation of coefficient estimations from individual machines. We study the upper limit of the order of the number of machines for this computing environment, which, if fulfilled, guarantees that the proposed estimator converges at a comparable rate to that of the oracle estimator. In addition, we also provide two further modifications for distributed systems including (i) a divide-and-conquer approximation and (ii) a nonparametric counterpart for censored quantile regression. Numerical experiments are conducted to compare the proposed and the existing estimators. The promising results demonstrate the computation efficiency of the proposed methods. Finally, for practical concerns, a cross-validation procedure is also developed which can better select the hyperparameters for the proposed methodologies.
