Reduced bias nonparametric lifetime density and hazard estimation

Arthur Berg (), Dimitris Politis, Kagba Suaray and Hui Zeng
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Arthur Berg: Penn State College of Medicine
Dimitris Politis: University of California
Kagba Suaray: California State University Long Beach
Hui Zeng: Penn State College of Medicine

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2020, vol. 29, issue 3, No 5, 704-727

Abstract: 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.

Keywords: Bandwidth estimation; Density estimation; Fourier transform; Hazard function estimation; Infinite-order kernels; Nonparametric estimation; Survival analysis; 62G07; 62G20; 62N99 (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11749-019-00677-z

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