Abstract
In this paper, we develop a fully nonparametric approach for the estimation of the cumulative incidence function with Missing At Random right-censored competing risks data. We obtain results on the pointwise asymptotic normality as well as the uniform convergence rate of the proposed nonparametric estimator. A simulation study that serves two purposes is provided. First, it illustrates in detail how to implement our proposed nonparametric estimator. Second, it facilitates a comparison of the nonparametric estimator to a parametric counterpart based on the estimator of Lu and Liang (2008). The simulation results are generally very encouraging.
Original language | English |
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Journal | Statistics & Probability Letters |
Volume | 89 |
Issue number | June |
Pages (from-to) | 1-7 |
ISSN | 0167-7152 |
DOIs | |
Publication status | Published - 1. Jun 2014 |
Keywords
- Cumulative incidence function
- Inverse probability weighting
- Kernel estimation
- Local linear estimation
- Martingale central limit theorem