Advanced Studies in Behaviormetrics and Data Science: Essays in Honor of Akinori Okada

by Tadashi Imaizumi (Editor), Atsuho Nakayama (Editor), Satoru Yokoyama (Editor)

Overview

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.

Product Details

ISBN-13:	9789811526992
Publisher:	Springer Nature Singapore
Publication date:	04/18/2020
Series:	Behaviormetrics: Quantitative Approaches to Human Behavior , #5
Edition description:	1st ed. 2020
Pages:	472
Product dimensions:	6.10(w) x 9.25(h) x (d)

About the Author

Tadashi Imaizumi is a professor at Tama University.
Atsuho Nakayama is an associate professor at Tokyo Metropolitan University.
Satoru Yokoyama is an associate professor at Aoyama Gakuin University.

Table of Contents

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.