Properties of an MLE algorithm for the multivariate linear model with a separable covariance matrix structure

Anna Szczepańska-Álvarez; Bogna Zawieja; Adolfo Álvarez

doi:10.2478/bile-2021-0005

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Properties of an MLE algorithm for the multivariate linear model with a separable covariance matrix structure

Biometrical Letters

Volume 58 (2021): Issue 1 (June 2021)

By: Anna Szczepańska-Álvarez, Bogna Zawieja and Adolfo Álvarez

Open Access

|Jun 2021

Abstract

In this paper we present properties of an algorithm to determine the maximum likelihood estimators of the covariance matrix when two processes jointly affect the observations. Additionally, one process is partially modeled by a compound symmetry structure. We perform a simulation study of the properties of an iteratively determined estimator of the covariance matrix.

References

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Articles in this issue

DOI: https://doi.org/10.2478/bile-2021-0005 | Journal eISSN: 2199-577X | Journal ISSN: 1896-3811

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Language: English

Page range: 69 - 79

Published on: Jun 24, 2021

Published by: Polish Biometric Society

In partnership with: Paradigm Publishing Services

Publication frequency: 2 issues per year

Keywords:

compound symmetry structure,

Kronecker product,

maximum likelihood estimation,

convergence of algorithm,

bias of estimator

Related subjects:

Life sciences,

Bioinformatics,

Life sciences, other,

Mathematics,

Probability and statistics,

Applied mathematics

© 2021 Anna Szczepańska-Álvarez, Bogna Zawieja, Adolfo Álvarez, published by Polish Biometric Society
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 58 (2021): Issue 1 (June 2021)