The Resource Rankings and preferences : new results in weighted correlation and weighted principal component analysis with applications, Joaquim Pinto da Costa

Rankings and preferences : new results in weighted correlation and weighted principal component analysis with applications, Joaquim Pinto da Costa

Label
Rankings and preferences : new results in weighted correlation and weighted principal component analysis with applications
Title
Rankings and preferences
Title remainder
new results in weighted correlation and weighted principal component analysis with applications
Statement of responsibility
Joaquim Pinto da Costa
Creator
Author
Subject
Genre
Language
eng
Summary
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
Member of
Cataloging source
N$T
http://library.link/vocab/creatorName
Costa, Joaquim Pinto da
Dewey number
519.536
Index
no index present
LC call number
HA31.3
Literary form
non fiction
Nature of contents
dictionaries
Series statement
SpringerBriefs in statistics
http://library.link/vocab/subjectName
  • Correlation (Statistics)
  • MATHEMATICS
  • MATHEMATICS
  • Correlation (Statistics)
Label
Rankings and preferences : new results in weighted correlation and weighted principal component analysis with applications, Joaquim Pinto da Costa
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-3-662-48344-2
Instantiates
Publication
Antecedent source
unknown
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Preface; Contents; 1 Introduction; 1.1 Some Motivating Applications; 1.2 Weighted Correlation and Applications; 1.3 Organization of the Book; 2 The Weighted Rank Correlation Coefficient rW; 2.1 Introduction; 2.2 Rank Correlation; 2.3 Weighted Rank Measure of Correlation; 2.4 Properties of the Distribution of rW Under the Null Hypothesis of Independence; 2.4.1 Linear Rank Statistics; 2.4.2 Exact and Asymptotic Distribution of rW Under the Null Hypothesis of Independence; 2.4.3 Simulations; 2.4.4 Comparison Between rW and rS; 2.5 The Asymptotic Distribution of rW for the General Case
  • 2.5.1 sum3i=1Ain is Asymptotically Normal Distributed 2.5.2 B*1n is Asymptotically Negligible ; 2.5.3 rW is Asymptotically Normal Distributed ; 2.6 Examples of Application of rW ; 2.7 Conclusions About rW; 3 The Weighted Rank Correlation Coefficient rW2; 3.1 Introduction; 3.2 The Formula of the Coefficient rW2 in the Case of Ties; 3.3 Comparison Between the Three Coefficients rW2, rW and rS; 3.4 A New Way of Developing Weighted Correlation Coefficients; 4 A Weighted Principal Component Analysis, WPCA1; Application to Gene Expression Data; 4.1 Introduction
  • 4.2 A New Weighted Version of PCA4.3 Preliminary Version of Weighted Principal Component Analysis Using rW; 4.4 Weighted Principal Component Analysys, WPCA1, Using rW2; 4.4.1 Robustness to Outliers and Noise; 4.5 A New Method for Selecting Relevant Genes in Microarray Data; 4.5.1 Support Vector Machines Classification: Results for Genes Chosen with SAM, PDeig, and with Our Method for WPCA and PCA; 4.5.2 Analysis of the Chosen Genes; 4.6 Conclusions; 5 A Weighted Principal Component Analysis (WPCA2) for Time Series Data; 5.1 Introduction; 5.2 Motivation and Definition
  • 5.3 Weight Functions: Examples5.4 Applications; 5.5 Final Remarks; 6 Weighted Clustering of Time Series; 6.1 Introduction; 6.2 Applications; Appendix; References
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9783662483442
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Note
SpringerLink
Other control number
10.1007/978-3-662-48344-2
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • (OCoLC)921302101
  • (OCoLC)ocn921302101
Label
Rankings and preferences : new results in weighted correlation and weighted principal component analysis with applications, Joaquim Pinto da Costa
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-3-662-48344-2
Publication
Antecedent source
unknown
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Preface; Contents; 1 Introduction; 1.1 Some Motivating Applications; 1.2 Weighted Correlation and Applications; 1.3 Organization of the Book; 2 The Weighted Rank Correlation Coefficient rW; 2.1 Introduction; 2.2 Rank Correlation; 2.3 Weighted Rank Measure of Correlation; 2.4 Properties of the Distribution of rW Under the Null Hypothesis of Independence; 2.4.1 Linear Rank Statistics; 2.4.2 Exact and Asymptotic Distribution of rW Under the Null Hypothesis of Independence; 2.4.3 Simulations; 2.4.4 Comparison Between rW and rS; 2.5 The Asymptotic Distribution of rW for the General Case
  • 2.5.1 sum3i=1Ain is Asymptotically Normal Distributed 2.5.2 B*1n is Asymptotically Negligible ; 2.5.3 rW is Asymptotically Normal Distributed ; 2.6 Examples of Application of rW ; 2.7 Conclusions About rW; 3 The Weighted Rank Correlation Coefficient rW2; 3.1 Introduction; 3.2 The Formula of the Coefficient rW2 in the Case of Ties; 3.3 Comparison Between the Three Coefficients rW2, rW and rS; 3.4 A New Way of Developing Weighted Correlation Coefficients; 4 A Weighted Principal Component Analysis, WPCA1; Application to Gene Expression Data; 4.1 Introduction
  • 4.2 A New Weighted Version of PCA4.3 Preliminary Version of Weighted Principal Component Analysis Using rW; 4.4 Weighted Principal Component Analysys, WPCA1, Using rW2; 4.4.1 Robustness to Outliers and Noise; 4.5 A New Method for Selecting Relevant Genes in Microarray Data; 4.5.1 Support Vector Machines Classification: Results for Genes Chosen with SAM, PDeig, and with Our Method for WPCA and PCA; 4.5.2 Analysis of the Chosen Genes; 4.6 Conclusions; 5 A Weighted Principal Component Analysis (WPCA2) for Time Series Data; 5.1 Introduction; 5.2 Motivation and Definition
  • 5.3 Weight Functions: Examples5.4 Applications; 5.5 Final Remarks; 6 Weighted Clustering of Time Series; 6.1 Introduction; 6.2 Applications; Appendix; References
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9783662483442
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Note
SpringerLink
Other control number
10.1007/978-3-662-48344-2
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • (OCoLC)921302101
  • (OCoLC)ocn921302101

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