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 language | English (US) |
|---|---|
| Journal | Test |
| DOIs | |
| State | Accepted/In press - Jan 1 2019 |
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All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Statistics, Probability and Uncertainty
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Reduced bias nonparametric lifetime density and hazard estimation. / Berg, Arthur; Politis, Dimitris; Suaray, Kagba; Zeng, Hui.
In: Test, 01.01.2019.Research output: Contribution to journal › Article
TY - JOUR
T1 - Reduced bias nonparametric lifetime density and hazard estimation
AU - Berg, Arthur
AU - Politis, Dimitris
AU - Suaray, Kagba
AU - Zeng, Hui
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85071099222&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071099222&partnerID=8YFLogxK
U2 - 10.1007/s11749-019-00677-z
DO - 10.1007/s11749-019-00677-z
M3 - Article
AN - SCOPUS:85071099222
JO - Trabajos de Estadistica Y de Investigacion Operativa
JF - Trabajos de Estadistica Y de Investigacion Operativa
SN - 0041-0241
ER -