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Data Analysis, Classification and the Forward Search
This book presents new developments in data analysis, classification and multivariate statistics, and in their algorithmic implementation. The volume offers contributions to the theory of clustering and discrimination, multidimensional data analysis, data mining, and robust statistics with a special emphasis on the novel Forward Search approach. Many papers provide significant insight in a wide range of fields of application. Customer satisfaction and service evaluation are two examples of such emerging fields.
New Developments in Psychometrics
At the International Meeting of the Psychometric Society in Osaka, Japan, more than 300 participants from 19 countries gathered to discuss recent developments in the theory and application of psychometrics. This volume of proceedings includes papers on methods of psychometrics such as the structural equation model and item response theory. The book is in eight major sections: keynote speeches and invited lectures; structural equation modeling and factor analysis; IRT and adaptive testing; multivariate statistical methods; scaling; classification methods; and independent and principal component analysis. The 80 papers collected here provide a valuable source of information for all who are concerned with psychometrics, mathematical and statistical applications, and data analysis in psychological and behavioral sciences.
COMPSTAT
This Volume contains the Keynote, Invited and Full Contributed papers presented at COMPSTAT'98. A companion volume (Payne & Lane, 1998) contains papers describing the Short Communications and Posters. COMPSTAT is a one-week conference held every two years under the auspices of the International Association of Statistical Computing, a section of the International Statistical Institute. COMPSTAT'98 is organised by IACR-Rothamsted, IACR-Long Ashton, the University of Bristol Department of Mathematics and the University of Bath Department of Mathematical Sciences. It is taking place from 24-28 August 1998 at University of Bristol. Previous COMPSTATs (from 1974-1996) were in Vienna, Berlin, Leide...
Nonlinear Estimation and Classification
Researchers in many disciplines face the formidable task of analyzing massive amounts of high-dimensional and highly-structured data. This is due in part to recent advances in data collection and computing technologies. As a result, fundamental statistical research is being undertaken in a variety of different fields. Driven by the complexity of these new problems, and fueled by the explosion of available computer power, highly adaptive, non-linear procedures are now essential components of modern "data analysis," a term that we liberally interpret to include speech and pattern recognition, classification, data compression and signal processing. The development of new, flexible methods combines advances from many sources, including approximation theory, numerical analysis, machine learning, signal processing and statistics. The proposed workshop intends to bring together eminent experts from these fields in order to exchange ideas and forge directions for the future.
Classification and Multivariate Analysis for Complex Data Structures
The growing capabilities in generating and collecting data has risen an urgent need of new techniques and tools in order to analyze, classify and summarize statistical information, as well as to discover and characterize trends, and to automatically bag anomalies. This volume provides the latest advances in data analysis methods for multidimensional data which can present a complex structure: The book offers a selection of papers presented at the first Joint Meeting of the Société Francophone de Classification and the Classification and Data Analysis Group of the Italian Statistical Society. Special attention is paid to new methodological contributions from both the theoretical and the applicative point of views, in the fields of Clustering, Classification, Time Series Analysis, Multidimensional Data Analysis, Knowledge Discovery from Large Datasets, Spatial Statistics.
Information and Classification
In many fields of science and practice large amounts of data and informationare collected for analyzing and visualizing latent structures as orderings or classifications for example. This volume presents refereed and revised versions of 52 papers selected from the contributions of the 16th AnnualConference of the "German Classification Society". The papers are organized in three major sections on Data Analysis and Classification (1), InformationRetrieval, Knowledge Processing and Software (2), Applications and Special Topics (3). Moreover, the papers were grouped and ordered within the major sections. So, in the first section we find papers on Classification Methods, Fuzzy Classification, Multidimensional Scaling, Discriminant Analysis and Conceptual Analysis. The second section contains papers on Neural Networks and Computational Linguisticsin addition to the mentioned fields. An essential part of the third section attends to Sequence Data and Tree Reconstruction as well as Data Analysis and Informatics in Medicine. As special topics the volume presents applications in Thesauri, Archaeology, Musical Science and Psychometrics.
Regression Inside Out
Linear regression analysis, with its many generalizations, is the predominant quantitative method used throughout the social sciences and beyond. The goal of the method is to study relations among variables. In this book, Schoon, Melamed and Breiger turn regression modeling inside out to put the emphasis on the cases (people, organizations, and nations) that comprise the variables. By re-analyzing influential published research, they reveal new insights and present a principled way to unlock a set of more nuanced interpretations than has previously been attainable. The emphasis is on intuition and examples that can be reproduced using the code and datasets provided. Relating their contributions to methodologies that operate under quite different philosophical assumptions, the authors advance multi-method social science and help to bridge the divide between quantitative and qualitative research. The result is a modern, accessible, and innovative take on extracting knowledge from data.
Knowledge in Ferment
Fundamental controversies, or basic oppositions between methods and approaches, occur in all fields of science and scholarship. Often these dilemmas arise at the nexus of science and society, or when several sciences or disciplines clash. Paradoxically, as much as one might prefer to do without them, these dilemmas are indispensable to the progress of scientific scholarship. Knowledge in Ferment collects the reflections of nineteen professors from Leiden University whose fields of expertise range from classics to quantum physics in order to evaluate the great controversies that dominate their fields and consistently form new paradigms that sustain the fermentation of knowledge and deliver remarkable new insights.
Visualization of Categorical Data
A unique and timely monograph, Visualization of Categorical Data contains a useful balance of theoretical and practical material on this important new area. Top researchers in the field present the books four main topics: visualization, correspondence analysis, biplots and multidimensional scaling, and contingency table models.This volume discusses how surveys, which are employed in many different research areas, generate categorical data. It will be of great interest to anyone involved in collecting or analyzing categorical data.* Correspondence Analysis* Homogeneity Analysis* Loglinear and Association Models* Latent Class Analysis* Multidimensional Scaling* Cluster Analysis* Ideal Point Discriminant Analysis* CHAID* Formal Concept Analysis* Graphical Models
Optimal Analysis of Qualitative Data with Illustrations
This is an introductory book on how to optimally analyze non-quantitative data, based on the author’s experiences over 60 years of research. The major message to the readers is that qualitative (non-quantitative) data are much more informative than quantitative data. This is good news for readers in applied areas of statistics such as those in the social sciences and marketing research, where qualitative data are everywhere. But how can one analyze qualitative data quantitatively and extract more information than from the sophisticated analysis of quantitative data? The key rests in illustrations of difficult topics in a way that anyone can understand. It is the author’s wish soon the use of AI will open a gate for simple means for optimal analysis of qualitative data, as illustrated throughout the book.
