Maximum-likelihood estimation of a log-concave density based on censored data

被引：0
|
作者：
Duembgen, Lutz [1 ]
Rufibach, Kaspar [2 ]
机构：
[1] Univ Bern, Inst Math Stat & Actuarial Sci, CH-3012 Bern, Switzerland
[2] F Hoffmann La Roche & Cie AG, Biostat Oncol, CH-4070 Basel, Switzerland
来源：
ELECTRONIC JOURNAL OF STATISTICS | 2014年 / 8卷
基金：
瑞士国家科学基金会;
关键词：
Active set algorithm; binning; cure parameter; expectation-maximization algorithm; interval-censoring; qualitative constraints; right-censoring; DISTRIBUTIONS;
D O I：
暂无
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：
We consider nonparametric maximum-likelihood estimation of a log-concave density in case of interval-censored, right-censored and binned data. We allow for the possibility of a subprobability density with an additional mass at +infinity, which is estimated simultaneously. The existence of the estimator is proved under mild conditions and various theoretical aspects are given, such as certain shape and consistency properties. An EM algorithm is proposed for the approximate computation of the estimator and its performance is illustrated in two examples.
引用
收藏
页码：1405 / 1437
页数：33
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