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.
Author(s): Joaquim Pinto da Costa (auth.)
Series: SpringerBriefs in Statistics
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 2015
Language: English
Pages: X, 91
Tags: Statistical Theory and Methods; Statistics for Life Sciences, Medicine, Health Sciences; Biostatistics
Front Matter....Pages i-x
Introduction....Pages 1-7
The Weighted Rank Correlation Coefficient \(r_W\) ....Pages 9-27
The Weighted Rank Correlation Coefficient \(r_{W2}\) ....Pages 29-38
A Weighted Principal Component Analysis, WPCA1; Application to Gene Expression Data....Pages 39-53
A Weighted Principal Component Analysis (WPCA2) for Time Series Data....Pages 55-67
Weighted Clustering of Time Series....Pages 69-73
Back Matter....Pages 75-91
