CONSTRUCTING EXPLICIT ESTIMATORS IN NONLINEAR REGRESSION PROBLEMS

被引：11

作者：

Linke, Yu Yu ^{[1
,2
]}

Borisov, I. S. ^{[1
,2
]}

机构：

[1] Sobolev Inst Math, Novosibirsk, Russia

[2] Novosibirsk State Univ, Novosibirsk, Russia

来源：

THEORY OF PROBABILITY AND ITS APPLICATIONS | 2018年 / 63卷 / 01期

基金：

俄罗斯基础研究基金会;

关键词：

nonlinear regression; explicit estimator; alpha(n)-consistency; asymptotic normality; one-step estimator; initial estimator; INITIAL ESTIMATORS; STEP;

D O I：

10.1137/S0040585X97T988897

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：

In the paper, we propose a general approach to constructing explicit consistent estimators for some classes of nonlinear regression models. These estimators can be used as initial ones in one-step estimation procedures capable of delivering, in a sense, optimal estimators in an explicit form.

引用

页码：22 / 44

页数：23

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