The kernel smoothed Nelson-Aalen estimator has been well investigated, but is unsuitable when some of the censoring indicators are missing. A representation introduced by Dikta, however, facilitates hazard estimation when there are missing censoring indicators. In this article, we investigate (i) a kernel smoothed semiparametric hazard estimator and (ii) a kernel smoothed "pre-smoothed" Nelson-Aalen estimator. We derive the asymptotic normality of the proposed estimators and compare their asymptotic variances.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Empirical estimators
- Liapounov central limit theorem
- Maximum likelihood estimator
- Missing at random
- Semiparametric random censorship models