Nonparametric Regression with Interval-Censored Data

In many medical studies, the prevalence of interval censored data is increasing due to periodic monitoring of the progression status of a disease. In nonparametric regression model, when the response variable is subjected to interval-censoring, the regression function could not be estimated by traditional methods directly. With the censored data, we construct a new response variable which has the same conditional expectation as the original one. Based on the new variable, we get a nearest neighbor estimator of the regression function. It is established that the estimator has strong consistency and asymptotic normality. The relevant simulation reports are given.