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Rankings and preferences[electronic ...

Costa, Joaquim Pinto da.

Rankings and preferences[electronic resource] :new results in weighted correlation and weighted principal component analysis with applications /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	519.536
書名/作者:	Rankings and preferences : new results in weighted correlation and weighted principal component analysis with applications // by Joaquim Pinto da Costa.
作者:	Costa, Joaquim Pinto da.
出版者:	Berlin, Heidelberg : : Springer Berlin Heidelberg :, 2015.
面頁冊數:	x, 91 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Correlation (Statistics)
標題:	Statistics.
標題:	Statistical Theory and Methods.
標題:	Statistics for Life Sciences, Medicine, Health Sciences.
標題:	Biostatistics.
ISBN:	9783662483442
ISBN:	9783662483435
內容註:	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.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-662-48344-2

Rankings and preferences[electronic resource] :new results in weighted correlation and weighted principal component analysis with applications /
Costa, Joaquim Pinto da.

Rankings and preferencesnew results in weighted correlation and weighted principal component analysis with applications /[electronic resource] :by Joaquim Pinto da Costa. - Berlin, Heidelberg :Springer Berlin Heidelberg :2015. - x, 91 p. :ill., digital ;24 cm. - SpringerBriefs in statistics,2191-544X. - SpringerBriefs in statistics..

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.

ISBN: 9783662483442

Standard No.: 10.1007/978-3-662-48344-2doiSubjects--Topical Terms:

635765
Correlation (Statistics)

LC Class. No.: HA31.3

Dewey Class. No.: 519.536

Rankings and preferences[electronic resource] :new results in weighted correlation and weighted principal component analysis with applications /
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520 $a 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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