MAXIMUM SMOOTHED LIKELIHOOD ESTIMATORS FOR THE INTERVAL CENSORING MODEL

被引：7

作者：

Groeneboom, Piet ^{[1
]}

机构：

[1] Delft Univ Technol, Delft Inst Appl Math, NL-2628 CD Delft, Netherlands

来源：

ANNALS OF STATISTICS | 2014年 / 42卷 / 05期

关键词：

Interval censoring; smoothed maximum likelihood estimator; maximum smoothed likelihood estimator; consistency; asymptotic distribution; integral equations; kernel estimators; ASYMPTOTICALLY OPTIMAL ESTIMATION; FUNCTIONALS;

D O I：

10.1214/14-AOS1256

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 study the maximum smoothed likelihood estimator (MSLE) for interval censoring, case 2, in the so-called separated case. Characterizations in terms of convex duality conditions are given and strong consistency is proved. Moreover, we show that, under smoothness conditions on the underlying distributions and using the usual bandwidth choice in density estimation, the local convergence rate is n(-2/5) and the limit distribution is normal, in contrast with the rate n(-1/3) of the ordinary maximum likelihood estimator.

引用

页码：2092 / 2137

页数：46

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