Reduced bias nonparametric lifetime density and hazard estimation

Arthur Berg, Dimitris Politis, Kagba Suaray, Hui Zeng

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
JournalTest
DOIs
StateAccepted/In press - 2019

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint Dive into the research topics of 'Reduced bias nonparametric lifetime density and hazard estimation'. Together they form a unique fingerprint.

Cite this