<abstract xmlns="http://www.w3.org/1999/xhtml"><p> In Annex H.5, the Guide to the Evaluation of Uncertainty in Measurement (GUM) [1] recognizes the necessity to analyze certain types of experiments by applying random effects ANOVA models. These belong to the more general family of linear mixed models that we focus on in the current paper. Extending the short introduction provided by the GUM, our aim is to show that the more general, linear mixed models cover a wider range of situations occurring in practice and can be beneficial when employed in data analysis of long-term repeated experiments. Namely, we point out their potential as an aid in establishing an uncertainty budget and as means for gaining more insight into the measurement process. We also comment on computational issues and to make the explanations less abstract, we illustrate all the concepts with the help of a measurement campaign conducted in order to challenge the uncertainty budget in calibration of accelerometers.</p></abstract>

In Annex H.5, the Guide to the Evaluation of Uncertainty in Measurement (GUM) [1] recognizes the necessity to analyze certain types of experiments by applying random effects ANOVA models. These belong to the more general family of linear mixed models that we focus on in the current paper. Extending the short introduction provided by the GUM, our aim is to show that the more general, linear mixed models cover a wider range of situations occurring in practice and can be beneficial when employed in data analysis of long-term repeated experiments. Namely, we point out their potential as an aid in establishing an uncertainty budget and as means for gaining more insight into the measurement process. We also comment on computational issues and to make the explanations less abstract, we illustrate all the concepts with the help of a measurement campaign conducted in order to challenge the uncertainty budget in calibration of accelerometers.

Linear Mixed Models: Gum and Beyond

Physikalisch-Technische Bundesanstalt Abbestr. 2-12, 10587 Berlin, Germany

Physikalisch-Technische Bundesanstalt Bundesallee 100, 38116 Braunschweig, Germany

Measurement Science Review

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

{"article-title":"Linear Mixed Models: Gum and Beyond"}

In Annex H.5, the Guide to the Evaluation of Uncertainty in Measurement (GUM) [1] recognizes the necessity to analyze certain types of experiments by applying...

Linear Mixed Models: Gum and Beyond

Publié en ligne: 08 mai 2014

Pages: 52 - 61

Reçu: 16 déc. 2013

Accepté: 28 mars 2014

DOI: https://doi.org/10.2478/msr-2014-0009

Mots clés
Linear mixed models, uncertainty, GUM, ANOVA, random effects

© by Barbora Arendacká

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linear Mixed Models: Gum and Beyond

Publié en ligne: 08 mai 2014

Pages: 52 - 61

Reçu: 16 déc. 2013

Accepté: 28 mars 2014

DOI: https://doi.org/10.2478/msr-2014-0009

Mots clésLinear mixed models, uncertainty, GUM, ANOVA, random effects

© by Barbora Arendacká

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Mots clés
Linear mixed models, uncertainty, GUM, ANOVA, random effects