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This book summarizes the methods and concepts of Statistical Implicative Analysis (SIA), created by Régis Gras in the 1980s to study, in a new way, the behavioural responses of French pupils to mathematics tests. Using a multidimensional, non-symmetrical data analysis method, SIA crosses a set of subjects or objects with a set of variables. It effectively complements traditional correlational and psychometric methods. SIA, through its various extensions, is today presented as a broad Artificial Intelligence method aimed at extracting trends and possible causalities in the form of rules, from a set of variables. It is based on the unlikeliness of the existence of these relationships, i.e. on the relative weakness of their counter-examples compared to what chance alone would produce. It establishes a dual topological relationship between the set of subjects and the set of variables. Many applications of this approach, driving forces or crucibles for the development of SIA, have concerned and still concern various fields such as didactics, evaluation and assessment, psychology, sociology, medicine, biology, economics, art history, and others. Key Features: Presents the foundations and representations of SIA. Provides extensions of variable sets and subjects. Includes a bonus exercise.
Statistical implicative analysis is a data analysis method created by Régis Gras almost thirty years ago which has a significant impact on a variety of areas ranging from pedagogical and psychological research to data mining. Statistical implicative analysis (SIA) provides a framework for evaluating the strength of implications; such implications are formed through common knowledge acquisition techniques in any learning process, human or artificial. This new concept has developed into a unifying methodology, and has generated a powerful convergence of thought between mathematicians, statisticians, psychologists, specialists in pedagogy and last, but not least, computer scientists specialized in data mining. This volume collects significant research contributions of several rather distinct disciplines that benefit from SIA. Contributions range from psychological and pedagogical research, bioinformatics, knowledge management, and data mining.
This book summarizes the methods and concepts of Statistical Implicative Analysis (SIA), created by Rgis Gras in the 1980s to study, in a new way, the behavioural responses of French pupils to mathematics tests. Using a multidimensional, non-symmetrical data analysis method, SIA crosses a set of subjects or objects with a set of variables. It effectively complements traditional correlational and psychometric methods. SIA, through its various extensions, is today presented as a broad Artificial Intelligence method aimed at extracting trends and possible causalities in the form of rules, from a set of variables. It is based on the unlikeliness of the existence of these relationships, i.e. on the relative weakness of their counter-examples compared to what chance alone would produce. It establishes a dual topological relationship between the set of subjects and the set of variables. Many applications of this approach, driving forces or crucibles for the development of SIA, have concerned and still concern various fields such as didactics, evaluation and assessment, psychology, sociology, medicine, biology, economics, art history, and others. Key Features: Presents the foundations and representations of SIA Provides extensions of variable sets and subjects Includes a bonus exercise
This title was first published in 2001. The objective of this book is to discuss specification and applications of new production, cost and profit functions. It is aimed at specialists in production, economic growth, costs, profits and applied econometrics in particular.
This book presents the elaboration model for the multivariate analysis of observational quantitative data. This model entails the systematic introduction of "third variables" to the analysis of a focal relationship between one independent and one dependent variable to ascertain whether an inference of causality is justified. Two complementary strategies are used: an exclusionary strategy that rules out alternative explanations such as spuriousness and redundancy with competing theories, and an inclusive strategy that connects the focal relationship to a network of other relationships, including the hypothesized causal mechanisms linking the focal independent variable to the focal dependent variable. The primary emphasis is on the translation of theory into a logical analytic strategy and the interpretation of results. The elaboration model is applied with case studies drawn from newly published research that serve as prototypes for aligning theory and the data analytic plan used to test it; these studies are drawn from a wide range of substantive topics in the social sciences, such as emotion management in the workplace, subjective age identification during the transition to adulthood, and the relationship between religious and paranormal beliefs. The second application of the elaboration model is in the form of original data analysis presented in two Analysis Journals that are integrated throughout the text and implement the full elaboration model. Using real data, not contrived examples, the text provides a step-by-step guide through the process of integrating theory with data analysis in order to arrive at meaningful answers to research questions.
This volume contains the papers from the Sixth Eugene Lukacs Symposium on ''Multidimensional Statistical Analysis and Random Matrices'', which was held at the Bowling Green State University, Ohio, USA, 29--30 March 1996. Multidimensional statistical analysis and random matrices have been the topics of great research. The papers presented in this volume discuss many varied aspects of this all-encompassing topic. In particular, topics covered include generalized statistical analysis, elliptically contoured distribution, covariance structure analysis, metric scaling, detection of outliers, density approximation, and circulant and band random matrices.
"Free CD contains several real and artificial data sets used in the book in SPSS, SYSTAT, and ASCII formats"--Cover
Using a new approach to ethnicity that underscores its relative territoriality, H. Zeynep Bulutgil brings together previously separate arguments that focus on domestic and international factors to offer a coherent theory of what causes ethnic cleansing. The author argues that domestic obstacles based on non-ethnic cleavages usually prevent ethnic cleansing whereas territorial conflict triggers this policy by undermining such obstacles. The empirical analysis combines statistical evaluation based on original data with comprehensive studies of historical cases in Central and Eastern Europe, as well as Bosnia, in the 1990s. The findings demonstrate how socio-economic cleavages curb radical factions within dominant groups whereas territorial wars strengthen these factions and pave the way for ethnic cleansing. The author further explores the theoretical and empirical extensions in the context of Africa. Its theoretical novelty and broad empirical scope make this book highly valuable to scholars of comparative and international politics alike.
During the last decade, the French-speaking scientific community developed a very strong research activity in the field of Knowledge Discovery and Management (KDM or EGC for “Extraction et Gestion des Connaissances” in French), which is concerned with, among others, Data Mining, Knowledge Discovery, Business Intelligence, Knowledge Engineering and SemanticWeb. The recent and novel research contributions collected in this book are extended and reworked versions of a selection of the best papers that were originally presented in French at the EGC 2009 Conference held in Strasbourg, France on January 2009. The volume is organized in four parts. Part I includes five papers concerned by various aspects of supervised learning or information retrieval. Part II presents five papers concerned with unsupervised learning issues. Part III includes two papers on data streaming and two on security while in Part IV the last four papers are concerned with ontologies and semantic.
Climate change can reasonably be expected to increase the frequency and intensity of a variety of potentially disruptive environmental events-slowly at first, but then more quickly. It is prudent to expect to be surprised by the way in which these events may cascade, or have far-reaching effects. During the coming decade, certain climate-related events will produce consequences that exceed the capacity of the affected societies or global systems to manage; these may have global security implications. Although focused on events outside the United States, Climate and Social Stress: Implications for Security Analysis recommends a range of research and policy actions to create a whole-of-government approach to increasing understanding of complex and contingent connections between climate and security, and to inform choices about adapting to and reducing vulnerability to climate change.