Rankings and Preferences
New Results in Weighted Correlation and Weighted Principal Component Analysis with Applications
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Beschreibung
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.
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.
Numerous applications help the reader to learn quickly Contains two special chapters on two very popular weighted correlation coefficients Describes an easy way of using weighted correlation with already existing statistical software Includes supplementary material: sn.pub/extras
Autor*in
Joaquim Pinto da Costa
Themen in »Rankings and Preferences«
microarray data principal component analysis ranking time series weighted correlation
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“This book describes newly developed methods of weighted correlation by the author and his collaborators. … This book is useful for those who want to learn a series of studies on weighted correlations by the author and his collaborators.” (Hidehiko Kamiya, Mathematical Reviews, August, 2016)
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