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Rankings and Preferences
(Englisch)
New Results in Weighted Correlation and Weighted Principal Component Analysis with Applications
Joaquim Pinto da Costa

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Rankings and Preferences

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Produktbeschreibung

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


Joaquim Pinto da Costa received his first degree in Applied Mathematics from Porto Universitiy (Portugal), his M. Sc. degree in Applied Statistics from Oxford University and his Ph.D. degree in Applied Mathematics from University of Rennes II (France). Since 199, he is Assistant Professor at the Mathematics Department of Porto University. His research interests include Statistics, Statistical Learning Theory, Pattern Recognition, Discriminant Analysis and Clustering, Data Analysis, Neural Networks, SVMs and Machine Learning.

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.


Introduction.- The Weighted Rank Correlation Coefficient rW.- The Weighted Rank Correlation Coefficient rW2 .- A Weighted Principal Component Analysis, WPCA1: Application to Gene Expression Data.- A Weighted Principal Component Analysis (WPCA2) for Time Series Data.- Weighted Clustering of Time Series.- Appendix.- References.


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 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)


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.



"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)




Über den Autor

Joaquim Pinto da Costa received his first degree in Applied Mathematics from Porto Universitiy (Portugal), his M. Sc. degree in Applied Statistics from Oxford University and his Ph.D. degree in Applied Mathematics from University of Rennes II (France). Since 199, he is Assistant Professor at the Mathematics Department of Porto University. His research interests include Statistics, Statistical Learning Theory, Pattern Recognition, Discriminant Analysis and Clustering, Data Analysis, Neural Networks, SVMs and Machine Learning.


Inhaltsverzeichnis



Introduction.- The Weighted Rank Correlation Coefficient rW.- The Weighted Rank Correlation Coefficient rW2 .- A Weighted Principal Component Analysis, WPCA1: Application to Gene Expression Data.- A Weighted Principal Component Analysis (WPCA2) for Time Series Data.- Weighted Clustering of Time Series.- Appendix.- References.


Klappentext

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



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