A1532
Title: Censored quantile regression based on multiply robust propensity scores
Authors: Xiaorui Wang - East China Normal University (China) [presenting]
Abstract: Censored quantile regression (QR) has elicited extensive research interest in recent years. One class of methods is based on an informative subset of a sample, selected via the propensity score (PS). PS can either be estimated using parametric methods, which poses the risk of misspecification, or obtained using nonparametric approaches, which suffers from the curse of dimensionality. We propose a new estimation method based on multiply robust PS for censored QR. This method only requires one of the multiple candidate models for PS to be correctly specified, and thus, it provides a certain level of resistance to the misspecification of parametric models. Large sample properties, such as the consistency and asymptotic normality of the proposed estimator, are thoroughly investigated. Extensive simulation studies are conducted to assess the performance of the proposed estimator. The proposed method is also applied to a study on human immunodeficiency viruses.
