2007/04/25 by Arthur Berg, Berg, Arthur, Dimitris N. Politis +5
Biochemistry, Genetics and Molecular Biology · Mathematics · #62G07 #62G20 #62N02 #FOS: Mathematics #MicroRNA in disease regulation #Statistical Methods and Inference #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.0704.3281
openalex publication_date 2007/04/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Kernel-based nonparametric hazard rate estimation is considered with a special class of infinite-order kernels that achieves favorable bias and mean square error properties. A fully automatic and adaptive implementation of a density and hazard rate estimator is proposed for randomly right censored data. Careful selection of the bandwidth in the proposed estimators yields estimates that are more efficient in terms of overall mean squared error performance, and in some cases achieves a nearly parametric convergence rate. Additionally, rapidly converging bandwidth estimates are presented for use in second-order kernels to supplement such kernel-based methods in hazard rate estimation. Simulations illustrate the improved accuracy of the proposed estimator against other nonparametric estimators of the density and hazard function. A real data application is also presented on survival data from 13,166 breast carcinoma patients.