Reduced bias nonparametric lifetime density and hazard estimation

• Arthur Berg ^[3] ; Dimitris Politis ^[1] ; Kagba Suaray ^[2] ; Hui Zeng ^[3]
  1. [1] University of California System

     University of California System

     Estados Unidos

     
  2. [2] California State University, Long Beach

     California State University, Long Beach

     Estados Unidos

     
  3. [3] Division of Biostatistics and Bioinformatics, Penn State College of Medicine, Hershey, PA, USA

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• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 29, Nº. 3, 2020, págs. 704-727
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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 square error performance, and in some cases, a nearly parametric convergence rate is achieved. 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.