<abstract xmlns="http://www.w3.org/1999/xhtml"><p> The paper integrates some earlier and the recent findings of the author in the area of network internal reliability and presents a consistent system of concepts in this respect. The concepts of outlier detection and outlier identification linked directly with the global model test and the outlier tests respectively, are shown as a basis for the concepts such as outlier detectability and outlier identifiability. Also, a four level classification of gross errors expressed in a form of a tree-diagram is presented including perceptible and imperceptible errors, detectable and undetectable errors and identifiable and unidentifiable errors. Their properties are given mainly in a descriptive way, deliberately limiting rigorous mathematical formulas to a necessary minimum. Understanding of different types of gross errors is useful in analyzing the results of the outlier detection and identification procedures as well as in designing the networks to make them duly robust to observation gross errors. It is of special importance for engineering surveys where quite often low-redundancy networks are used. Main objective of the paper is to demonstrate a clear and consistent system of basic concepts related to network internal reliability.</p></abstract>

The paper integrates some earlier and the recent findings of the author in the area of network internal reliability and presents a consistent system of concepts in this respect. The concepts of outlier detection and outlier identification linked directly with the global model test and the outlier tests respectively, are shown as a basis for the concepts such as outlier detectability and outlier identifiability. Also, a four level classification of gross errors expressed in a form of a tree-diagram is presented including perceptible and imperceptible errors, detectable and undetectable errors and identifiable and unidentifiable errors. Their properties are given mainly in a descriptive way, deliberately limiting rigorous mathematical formulas to a necessary minimum. Understanding of different types of gross errors is useful in analyzing the results of the outlier detection and identification procedures as well as in designing the networks to make them duly robust to observation gross errors. It is of special importance for engineering surveys where quite often low-redundancy networks are used. Main objective of the paper is to demonstrate a clear and consistent system of basic concepts related to network internal reliability.

Illustrative presentation of some basic concepts of network internal reliability with comments as regards engineering surveys

Warsaw University of Technology Faculy of Geodesy and Cartography Pl. Politechniki 1, 00-661 Warsaw, Poland

Reports on Geodesy and Geoinformatics

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

The paper integrates some earlier and the recent findings of the author in the area of network internal reliability and presents a consistent system of...

Illustrative presentation of some basic concepts of network internal reliability with comments as regards engineering surveys

Data publikacji: 14 lip 2016

Zakres stron: 54 - 59

DOI: https://doi.org/10.1515/rgg-2016-0021

Słowa kluczowe
unit redundancy, low-redundancy networks, detectability, identifiability, gross error classification

© 2016

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

Illustrative presentation of some basic concepts of network internal reliability with comments as regards engineering surveys

Data publikacji: 14 lip 2016

Zakres stron: 54 - 59

DOI: https://doi.org/10.1515/rgg-2016-0021

Słowa kluczoweunit redundancy, low-redundancy networks, detectability, identifiability, gross error classification

© 2016

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

Słowa kluczowe
unit redundancy, low-redundancy networks, detectability, identifiability, gross error classification