<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

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

Department of Mathematical and Statistical Methods

Collegium Da Vinci, Department of Informatics and Visual Communication

Biometrical Letters

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In this paper we present properties of an algorithm to determine the maximum likelihood estimators of the covariance matrix when two processes jointly affect...

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

Published Online: Jun 24, 2021

Page range: 69 - 79

DOI: https://doi.org/10.2478/bile-2021-0005

Keywords
compound symmetry structure, Kronecker product, maximum likelihood estimation, convergence of algorithm, bias of estimator

© 2021 Anna Szczepańska-Álvarez et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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

Published Online: Jun 24, 2021

Page range: 69 - 79

DOI: https://doi.org/10.2478/bile-2021-0005

Keywordscompound symmetry structure, Kronecker product, maximum likelihood estimation, convergence of algorithm, bias of estimator

© 2021 Anna Szczepańska-Álvarez et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
compound symmetry structure, Kronecker product, maximum likelihood estimation, convergence of algorithm, bias of estimator