Joaquim Pinto da Costa

Rankings and Preferences

New Results in Weighted Correlation and Weighted Principal Component Analysis with Applications

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• SpringerBriefs in Statistics
• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 978-3-662-48343-5
• 1st ed. 2015
• Seitenzahl: 104
• Erscheinungstermin: 25. September 2015
• Englisch
• Abmessung: 235mm x 155mm x 7mm
• Gewicht: 172g
• ISBN-13: 9783662483435
• ISBN-10: 3662483432
• Artikelnr.: 43379078

Autorenporträt

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.

Inhaltsangabe

Introduction.- The Weighted Rank Correlation Coefficient r_W.- The Weighted Rank Correlation Coefficient r_W2 .- 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.

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Introduction.- The Weighted Rank Correlation Coefficient r_W.- The Weighted Rank Correlation Coefficient r_W2 .- 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.

Rezensionen

"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)