Title: Nonparametric Regression with a Randomly Censored Independent Variable
Authors: Hui Jiang - Huazhong University of Science and Technology (China) [presenting]
Yingcun Xia - National University of Singapore (Singapore)
Lei Huang - Southwest Jiaotong University (China)
Abstract: We consider nonparametric estimation of regression functions when the covariate is subject to random censoring. Most existing studies have examined the estimation of linear regression models with censored covariates,but those methods are not applicable to explain nonlinear relationship between the response and covariates. In this paper, we propose a new method to estimate regression functions based on nonparametric estimation of conditional hazard rates. The asymptotic normality of our proposed estimator is established. Additionally, compared with the complete case method, both theoretical results and simulation studies demonstrate that the proposed nonparametric method could estimate unknown regression functions more accurately especially in the presence of heavy censoring. Finally, we illustrate and compare methods using the well-known dataset from a randomized placebo controlled clinical trial of the drug D-penicillamine (DPCA).