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

<p>The target of this paper is to offer a compact review of the so called distance methods in Statistics, which cover all the known estimation methods. Based on this fact we propose a new step, to adopt from Information Theory, the divergence measures, as distance methods, to compare two distributions, and not only to investigate if the means or the variances of the distributions are equal. Some useful results towards this line of thought are presented, adopting a compact form for all known divergence measures, and are appropriately analyzed for Biometrical, and not only, applications.</p>
</abstract>

The target of this paper is to offer a compact review of the so called distance methods in Statistics, which cover all the known estimation methods. Based on this fact we propose a new step, to adopt from Information Theory, the divergence measures, as distance methods, to compare two distributions, and not only to investigate if the means or the variances of the distributions are equal. Some useful results towards this line of thought are presented, adopting a compact form for all known divergence measures, and are appropriately analyzed for Biometrical, and not only, applications.

Considering distance measures in Statistics

Department of Informatics and Computer Engineering

Department of Public and Community Health

Biometrical Letters

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

{"article-title":"Considering distance measures in Statistics"}

The target of this paper is to offer a compact review of the so called distance methods in Statistics, which cover all the known estimation methods. Based on...

Considering distance measures in Statistics

Online veröffentlicht: 30. Juni 2022

Seitenbereich: 65 - 75

DOI: https://doi.org/10.2478/bile-2022-0006

Schlüsselwörter
Euclidean distance, information theory, bioassays

© 2022 Christos P. Kitsos et al., published by Sciendo

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

Considering distance measures in Statistics

Online veröffentlicht: 30. Juni 2022

Seitenbereich: 65 - 75

DOI: https://doi.org/10.2478/bile-2022-0006

SchlüsselwörterEuclidean distance, information theory, bioassays

© 2022 Christos P. Kitsos et al., published by Sciendo

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

Schlüsselwörter
Euclidean distance, information theory, bioassays