Nonlinear regression models with increasing numbers of unknown parameters

被引：3

作者：

Hajiyev, A. H. ^{[1
]}

Hajiyev, V. G. ^{[2
]}

机构：

[1] Azerbaijan Acad Sci, Inst Cybernet, Baku 370141, Azerbaijan

[2] Baku State Univ, AZ-1148 Baku, Azerbaijan

来源：

DOKLADY MATHEMATICS | 2009年 / 79卷 / 03期

关键词：

Covariance Matrix; Unknown Parameter; Error Variance; Iterative Procedure; Iteration Step;

D O I：

10.1134/S1064562409030107

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

An approach for determining least squares estimators and covariance matrix elements is proposed to construct a confidence interval for the unknown function in nonlinear regression models with an increasing number of unknown parameters. The number of unknown parameters depend on the number of observations and a least square estimator is constructed by the iterative procedure. The minimum number of iteration steps are found, which is helpful in finding the asymptotic normality. A random variable is derived that is found to be the normal equation for the least squares estimator. The least squares estimators are used to construct an asymptotic confidence interval for the unknown function in nonlinear regression model.

引用

页码：339 / 341

页数：3

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