Asymptotic normality of modified LS estimator for mixture of nonlinear regressions

被引：3

作者：

Miroshnichenko, Vitalii ^{[1
]}

Maiboroda, Rostyslav ^{[1
]}

机构：

[1] Taras Shevchenko Natl Univ Kyiv, Kiev, Ukraine

来源：

MODERN STOCHASTICS-THEORY AND APPLICATIONS | 2020年 / 7卷 / 04期

关键词：

Finite mixture model; nonlinear regression; mixture with varying concentrations; semiparametric estimation; confidence ellipsoid;

D O I：

10.15559/20-VMSTA167

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 a mixture with varying concentrations in which each component is described by a nonlinear regression model. A modified least squares estimator is used to estimate the regressions parameters. Asymptotic normality of the derived estimators is demonstrated. This result is applied to confidence sets construction. Performance of the confidence sets is assessed by simulations.

引用

页码：435 / 448

页数：14

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