Dewey Class |
519.5 |
Title |
Advanced Studies in Behaviormetrics and Data Science ([EBook]) : Essays in Honor of Akinori Okada / / edited by Tadashi Imaizumi, Atsuho Nakayama, Satoru Yokoyama. |
Added Personal Name |
Imaizumi, Tadashi |
Nakayama, Atsuho |
Yokoyama, Satoru |
Other name(s) |
SpringerLink (Online service) |
Edition statement |
1st ed. 2020. |
Publication |
Singapore : : Springer Singapore : : Imprint: Springer, , 2020. |
Physical Details |
XV, 472 p. 136 illus., 69 illus. in color. : online resource. |
Series |
Behaviormetrics: Quantitative Approaches to Human Behavior 2524-4027 ; ; 5 |
ISBN |
9789811527005 |
Summary Note |
This book focuses on the latest developments in behaviormetrics and data science, covering a wide range of topics in data analysis and related areas of data science, including analysis of complex data, analysis of qualitative data, methods for high-dimensional data, dimensionality reduction, visualization of such data, multivariate statistical methods, analysis of asymmetric relational data, and various applications to real data. In addition to theoretical and methodological results, it also shows how to apply the proposed methods to a variety of problems, for example in consumer behavior, decision making, marketing data, and social network structures. Moreover, it discuses methodological aspects and applications in a wide range of areas, such as behaviormetrics; behavioral science; psychology; and marketing, management and social sciences. Combining methodological advances with real-world applications collected from a variety of research fields, the book is a valuable resource for researchers and practitioners, as well as for applied statisticians and data analysts. .: |
Contents note |
Co-clustering for object by variable data matrices -- How to use the Hermitian Form Model for asymmetric MDS -- Asymmetric scaling models for square contingency tables: points, circles, arrows, and odds ratios -- Comparing partitions of the Petersen graph -- Minkowski distances and standardisation for clustering and classification on high dimensional data. . |
Mode of acces to digital resource |
Digital book. Cham Springer Nature 2020. - Mode of access: World Wide Web. System requirements: Internet Explorer 6.0 (or higher) or Firefox 2.0 (or higher). Available as searchable text in PDF format |
System details note |
- Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users). |
Internet Site |
https://doi.org/10.1007/978-981-15-2700-5 |
Links to Related Works |
Subject References:
Authors:
Corporate Authors:
Series:
Classification:
|