
Graph Sampling
Li-Chun Zhang(Author)
Chapman & Hall/CRC (Publisher)
1st Edition
Published on 27. December 2021
Book
Hardback
122 pages
978-1-032-06708-7 (ISBN)
Description
Many technological, socio-economic, environmental, biomedical phenomena exhibit an underlying graph structure. Valued graph allows one to incorporate the connections or links among the population units in addition. The links may provide effectively access to the part of population that is the primary target, which is the case for many unconventional sampling methods, such as indirect, network, line-intercept or adaptive cluster sampling. Or, one may be interested in the structure of the connections, in terms of the corresponding graph properties or parameters, such as when various breadth- or depth-first non-exhaustive search algorithms are applied to obtain compressed views of large often dynamic graphs.
Graph sampling provides a statistical approach to study real graphs from either of these perspectives. It is based on exploring the variation over all possible sample graphs (or subgraphs) which can be taken from the given population graph, by means of the relevant known sampling probabilities. The resulting design-based inference is valid whatever the unknown properties of the given real graphs.
One-of-a-kind treatise of multidisciplinary topics relevant to statistics, mathematics and data science.
Probabilistic treatment of breadth-first and depth-first non-exhaustive search algorithms in graphs.
Presenting cutting-edge theory and methods based on latest research.
Pathfinding for future research on sampling from real graphs.
Graph Sampling can primarily be used as a resource for researchers working with sampling or graph problems, and as the basis of an advanced course for post-graduate students in statistics, mathematics and data science.
Graph sampling provides a statistical approach to study real graphs from either of these perspectives. It is based on exploring the variation over all possible sample graphs (or subgraphs) which can be taken from the given population graph, by means of the relevant known sampling probabilities. The resulting design-based inference is valid whatever the unknown properties of the given real graphs.
One-of-a-kind treatise of multidisciplinary topics relevant to statistics, mathematics and data science.
Probabilistic treatment of breadth-first and depth-first non-exhaustive search algorithms in graphs.
Presenting cutting-edge theory and methods based on latest research.
Pathfinding for future research on sampling from real graphs.
Graph Sampling can primarily be used as a resource for researchers working with sampling or graph problems, and as the basis of an advanced course for post-graduate students in statistics, mathematics and data science.
More details
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Illustrations
37 s/w Abbildungen, 37 s/w Zeichnungen, 21 s/w Tabellen
21 Tables, black and white; 37 Line drawings, black and white; 37 Illustrations, black and white
Dimensions
Height: 222 mm
Width: 145 mm
Thickness: 11 mm
Weight
314 gr
ISBN-13
978-1-032-06708-7 (9781032067087)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Li-Chun Zhang
Graph Sampling
E-Book
12/2021
1st Edition
Chapman & Hall/CRC
€31.49
Available for download

Li-Chun Zhang
Graph Sampling
E-Book
12/2021
1st Edition
Chapman & Hall/CRC
€31.49
Available for download
Person
Li-Chun Zhang is Professor of Social Statistics at the University of Southampton, Senior Researcher at Statistics Norway, and Professor of Official Statistics at the University of Oslo. He has researched and published on topics such as finite population sampling design and coordination, graph sampling, machine learning, sample survey estimation, non-response, measurement errors, small area estimation, index number calculations, editing and imputation, register-based statistics, population size estimation, statistical matching, record linkage.
Content
1. General introduction
2. Bipartite incidence graph sampling and weighting
3. Strategy BIGS-IWE
4. Adaptive cluster sampling
5. Snowball sampling
6. Targeted random walk sampling
2. Bipartite incidence graph sampling and weighting
3. Strategy BIGS-IWE
4. Adaptive cluster sampling
5. Snowball sampling
6. Targeted random walk sampling