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Methods for the Analysis of Asymmetric Proximity Data / by Giuseppe Bove, Akinori Okada, Donatella Vicari
(Behaviormetrics: Quantitative Approaches to Human Behavior. ISSN:25244035 ; 7)

Edition 1st ed. 2021.
Publisher (Singapore : Springer Nature Singapore : Imprint: Springer)
Year 2021
Size X, 194 p. 68 illus., 39 illus. in color : online resource
Authors *Bove, Giuseppe author
Okada, Akinori author
Vicari, Donatella author
SpringerLink (Online service)
Subjects LCSH:Statistics 
LCSH:Mathematical statistics—Data processing
FREE:Applied Statistics
FREE:Statistics and Computing
FREE:Statistical Theory and Methods
Notes Introduction -- Methods for direct representation of asymmetry -- Analysis of symmetry and skew-symmetry -- Cluster analysis for asymmetry -- Multiway models -- Software.
This book provides an accessible introduction and practical guidelines to apply asymmetric multidimensional scaling, cluster analysis, and related methods to asymmetric one-mode two-way and three-way asymmetric data. A major objective of this book is to present to applied researchers a set of methods and algorithms for graphical representation and clustering of asymmetric relationships. Data frequently concern measurements of asymmetric relationships between pairs of objects from a given set (e.g., subjects, variables, attributes,…), collected in one or more matrices. Examples abound in many different fields such as psychology, sociology, marketing research, and linguistics and more recently several applications have appeared in technological areas including cybernetics, air traffic control, robotics, and network analysis. The capabilities of the presented algorithms are illustrated by carefully chosen examples and supported by extensive data analyses. A review of the specialized statistical software available for the applications is also provided. This monograph is highly recommended to readers who need a complete and up-to-date reference on methods for asymmetric proximity data analysis
HTTP:URL=https://doi.org/10.1007/978-981-16-3172-6
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Springer eBooks 9789811631726
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Material Type E-Book
Classification LCC:QA276-280
DC23:519
ID 4000140722
ISBN 9789811631726

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