Rankings and Preferences: New Results in Weighted Correlation and Weighted Principal Component Analysis with Applications

Springer
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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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About the author

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.
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Additional Information

Publisher
Springer
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Published on
Sep 15, 2015
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Pages
91
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ISBN
9783662483442
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Best For
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Language
English
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Genres
Mathematics / Probability & Statistics / General
Mathematics / Probability & Statistics / Stochastic Processes
Medical / Biostatistics
Science / Life Sciences / General
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Content Protection
This content is DRM protected.
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