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Let { X i , 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. [ABSTRACT FROM AUTHOR]

In this paper, we consider the estimation of the regression function when the interest variable is subject to random censorship and the data satisfy some dependency conditions. We show that the new estimate [defined in Guessoum, Z., and Ould Saïd, E. (2008),‘On Nonparametric Estimation of the Regression Function Under Censorship Model’, Statistics & Decisions, 26, 159–177] suitably normalised is asymptotically normally distributed and the asymptotic variance is given explicitly. An application to confidence bands is given. Some simulations are drawn to lend further support to our theoretical results and to compare finite samples sizes with different rates of censoring and dependence. [ABSTRACT FROM PUBLISHER]