Nonparametric density estimation in presence of bias and censoring

被引：13
|
作者：
Brunel, E. [1 ]
Comte, F. [2 ]
Guilloux, A. [3 ]
机构：
[1] IUT Paris V, MAP5, CNRS, UMR 8145, Paris, France
[2] Univ Paris 05, CNRS, MAP5, UMR 8145, Paris, France
[3] Univ Paris 06, LSTA, Paris, France
来源：
TEST | 2009年 / 18卷 / 01期
关键词：
Adaptive estimation; Minimax rate; Biased data; Right-censoring; Nonparametric penalized contrast estimator; HAZARD RATE ESTIMATION; SELECTION; DISTRIBUTIONS; SAMPLES; BOUNDS;
D O I：
10.1007/s11749-007-0075-5
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 projection estimator methods for the nonparametric estimation of the density of i.i.d. biased observations with a general known bias function w and under right censoring. Adaptive procedures to catch the optimal estimator among a collection by contrast penalization are investigated and proved to give efficient estimators with optimal nonparametric rates of convergence. Monte-Carlo experiments complete the study and illustrate the method.
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页码：166 / 194
页数：29
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