Reduced bias nonparametric lifetime density and hazard estimation

被引:0
作者
Berg, Arthur [1 ]
Politis, Dimitris [2 ]
Suaray, Kagba [3 ]
Zeng, Hui [1 ]
机构
[1] Penn State Coll Med, Div Biostat & Bioinformat, Hershey, PA 17033 USA
[2] Univ Calif San Diego, Dept Math, La Jolla, CA 92093 USA
[3] Calif State Univ Long Beach, Dept Math & Stat, Long Beach, CA 90840 USA
关键词
Bandwidth estimation; Density estimation; Fourier transform; Hazard function estimation; Infinite-order kernels; Nonparametric estimation; Survival analysis; BANDWIDTH SELECTION; CENSORED-DATA; KERNELS; MODEL;
D O I
10.1007/s11749-019-00677-z
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
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.
引用
收藏
页码:704 / 727
页数:24
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