This book examines in detail the correlation, more precisely the weighted correlation and applications involving rankings. A general application is the evaluation of methods to predict rankings. Others involve rankings representing human preferences to infer user preferences; the use of weighted correlation with microarray data and those in the domain of time series. In this book we present new weighted correlation coefficients and new methods of weighted principal component analysis. We also introduce new methods of dimension reduction and clustering for time series data and describe some theoretical results on the weighted correlation coefficients in separate sections.
Zusammenfassung: The book provides a detailed quantitative study and characterization of the physics of the thermal and viscoelastic behavior of mainly amorphous materials, and addresses a readership of both undergraduate (Part I and the two first chapters of Part II) and graduate students and junior researchers (Parts II and III). Though the discussion and examples concentrate on polymer materials, Part II illustrates the potential universality of the proposed most recent treatment - a Cooperative Theory of Materials Dynamics (CTMD) - and its ability to portray the 11 major physical characteristics of the materials' behavior by an alternative view of the thermal equilibrium and non-equilibrium dynamics at the "micro-scale", the still challenging problem of the glass transition and glass transition temperature, how partial crosslinking or crystallization limits the response, the expected impact of molecular packing, and of a few other open challenges. Part III discusses three specific domains where new applications and extensions of CTMD might be explored, while three Appendixes collect a few quantitative details and extensions of the treatment
This book examines in detail the correlation, more precisely the weighted correlation and applications involving rankings. A general application is the evaluation of methods to predict rankings. Others involve rankings representing human preferences to infer user preferences; the use of weighted correlation with microarray data and those in the domain of time series. In this book we present new weighted correlation coefficients and new methods of weighted principal component analysis. We also introduce new methods of dimension reduction and clustering for time series data and describe some theoretical results on the weighted correlation coefficients in separate sections.
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