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Multidimensional Nonlinear Descriptive Analysis
 
 
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Multidimensional Nonlinear Descriptive Analysis [Hardcover]

Shizuhiko Nishisato (Author)

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Book Description

1584886129 978-1584886129 June 26, 2006 1
Quantification of categorical, or non-numerical, data is a problem that scientists face across a wide range of disciplines. Exploring data analysis in various areas of research, such as the social sciences and biology, Multidimensional Nonlinear Descriptive Analysis presents methods for analyzing categorical data that are not necessarily sampled randomly from a normal population and often involve nonlinear relations.

This reference not only provides an overview of multidimensional nonlinear descriptive analysis (MUNDA) of discrete data, it also offers new results in a variety of fields. The first part of the book covers conceptual and technical preliminaries needed to understand the data analysis in subsequent chapters. The next two parts contain applications of MUNDA to diverse data types, with each chapter devoted to one type of categorical data, a brief historical comment, and basic skills peculiar to the data types. The final part examines several problems and then concludes with suggestions for future progress.

Covering both the early and later years of MUNDA research in the social sciences, psychology, ecology, biology, and statistics, this book provides a framework for potential developments in even more areas of study.


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Inside This Book (learn more)
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
successive categories data, generalized forced classification, partial correspondence analysis, quantifying categorical data, normed weights, dominance data, dual scaling, nonlinear relations between variables, paired comparison data, composite axis, multidimensional decomposition, symmetric scaling, projected weights, unidimensional analysis, joint plot, outlier responses, space discrepancy, multidimensional unfolding, dominance table, multidimensional data analysis, multiple correspondence analysis, optimal scaling, criterion item, dominance numbers, criterion pair
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Ter Braak, Peter Tung, Psychometric Society, The Netherlands, Shizuhiko Nishisato
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