Fast calculations of Jackknife covariance matrix estimator

Мірошниченко, V. O.

doi:10.17721/1812-5409.2021/1.3

Fast calculations of Jackknife covariance matrix estimator

Тип публікації :

Стаття

Дата випуску :

2021

Автор(и) :

Мірошниченко, V. O.

Мова основного тексту :

Англійська

eKNUTSHIR URL :

https://ir.library.knu.ua/handle/15071834/26053

DOI :

10.17721/1812-5409.2021/1.3

Журнал :

Bulletin of Taras Shevchenko National University of Kyiv. Physics and Mathematics

Випуск :

1

ISSN :

1812-5409

Початкова сторінка :

27

Кінцева сторінка :

36

Цитування :

[APA 7] Мірошниченко, V. O. (2021). Fast calculations of Jackknife covariance matrix estimator. Bulletin of Taras Shevchenko National University of Kyiv. Physics and Mathematics, (1), 27–36. https://doi.org/10.17721/1812-5409.2021/1.3

[ДСТУ] Мірошниченко V. O. Fast calculations of Jackknife covariance matrix estimator. Bulletin of Taras Shevchenko National University of Kyiv. Physics and Mathematics. 2021. no. 1. P. 27—36. DOI: 10.17721/1812-5409.2021/1.3 (date of access: 18.07.2026).

We consider data in which each observed subject belongs to one of different subpopulations (components). The true number of component which a subject belongs to is unknown, but the researcher knows the probabilities that a subject belongs to a given component (concentration of the component in the mixture). The concentrations are different for different observations. So the distribution of the observed data is a mixture of components’ distributions with varying concentrations. A set of variables is observed for each subject. Dependence between these variables is described by a nonlinear regression model. The coefficients of this model are different for different components. Normality of estimator for nonlinear regression parameters is demonstrated under general assumptions. A mixture of logistic regression models with continuous response is considered as an example. In the paper we construct confidence ellipsoids for the regression parameters based on the modified least squares estimators. The covariances of these estimators are estimated by the multiple modifications of jackknife technique. Performance of the obtained confidence ellipsoids is assessed by simulations.
Pages of the article in the issue: 27 - 36
Language of the article: Ukrainian

Ключові слова :

generalized estimation equations asymptotic behavior mixture model non-linear regression minimax weights covariance estimator асимптотична поведінка метод оцінюючих рівнянь модель суміші нелінійна регресія мінімаксні ваги оцінка коваріації jackknife

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Формат :

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