Asymptotic properties of asymmetric kernel estimators for non-negative and censored data

Let {Xi,i≥1} be a sequence of independent and identically distributed random variables with distribution function F and probability density function f. We propose new type of kernel estimators for density and hazard functions that perform well at the boundary, when the variable of interest is positive and right censored. The estimators are constructed using asymmetric kernels with expectation 1. We establish uniform strong consistency rates and we study asymptotic properties and normality of the resulting estimators. A large simulation study is conducted to comfort the theoretical results. An application to real data is done.