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.

引用

页码：166 / 194

页数：29

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