Nonlinear censored regression using synthetic data

被引：23

作者：

Delecroix, Michel ^{[1
,2
]}

Lopez, Olivier ^{[1
,3
]}

Patilea, Valentin ^{[1
,4
]}

机构：

[1] ENSAI, CREST, F-35172 Bruz, France

[2] Univ Paris 06, LSTA, F-75252 Paris 05, France

[3] Univ Rennes 1, IRMAR, F-35042 Rennes, France

[4] INSA Rennes, IRMAR, Rennes, France

来源：

SCANDINAVIAN JOURNAL OF STATISTICS | 2008年 / 35卷 / 02期

关键词：

asymptotic normality; consistency; Kaplan-Meier integral; nonlinear regression; right censoring; synthetic data;

D O I：

10.1111/j.1467-9469.2007.00591.x

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：

The problem of estimating a nonlinear regression model, when the dependent variable is randomly censored, is considered. The parameter of the model is estimated by least squares using synthetic data. Consistency and asymptotic normality of the least squares estimators are derived. The proofs are based on a novel approach that uses i.i.d. representations of synthetic data through Kaplan-Meier integrals. The asymptotic results are supported by a small simulation study.

引用

页码：248 / 265

页数：18

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