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Grade Models and Methods for Data Analysis With Applications for the Analysis of Data Populations

Aus der Reihe Studies in Fuzziness and Soft Computing

195,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

20.07.2012

Abbildungen

X, 477 p.

Herausgeber

Teresa Kowalczyk + weitere

Verlag

Springer Berlin

Seitenzahl

477

Maße (L/B/H)

23,5/15,5/2,7 cm

Gewicht

739 g

Auflage

Softcover reprint of the original 1st edition 2004

Sprache

Englisch

ISBN

978-3-642-53561-1

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

20.07.2012

Abbildungen

X, 477 p.

Herausgeber

Verlag

Springer Berlin

Seitenzahl

477

Maße (L/B/H)

23,5/15,5/2,7 cm

Gewicht

739 g

Auflage

Softcover reprint of the original 1st edition 2004

Sprache

Englisch

ISBN

978-3-642-53561-1

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: Grade Models and Methods for Data Analysis
  • Produktbild: Grade Models and Methods for Data Analysis
  • 1 Grade Data Analysis — A First Look.- 1.1 “Questions” from clients.- 1.2 About “Grade Models and Methods for Data Analysis”.- 1.3 Addressing the practitioner.- 1.4 Addressing the theorist.- 1.5 Regarding the analysis of data populations.- 1.6 Overview of Grade Data Analysis algorithms.- 1.7 Returning to the clients from the first page.- 1.8 Conclusion — Chapter 1.- 2 The Grade Approach.- 2.1 Introduction.- 2.2 Part 1: Quick start to the understanding of grade concepts.- 2.3 Steps to making a concentration curve.- 2.4 Quick Start summary.- 2.5 Preview of Part 2, and suggestions before your eventual study of the multivariate material.- 2.6 Part 2: Understanding concentration curves.- 2.7 Chapter Summary.- 3 Univariate Lilliputian Model I.- 3.1 Introduction.- 3.2 Lilliputian variables and their basic parameters.- 3.3 The main equivalence relation which creates the Univariate Lilliputian Model.- 3.4 Grade parameters.- 3.5 Appendix.- 4 Univariate Lilliputian Model II.- 4.1 Introduction.- 4.2 Lorenz Curve and Gini Index.- 4.3 Order oriented concentration curves.- 4.4 Dual concentration curve.- 4.5 Appendix.- 5 Asymmetry and the inverse concentration set.- 5.1 Introduction.- 5.2 Concentration curves with a common value of the concentration index.- 5.3 Links between asymmetry and opposite orderings.- 5.4 Asymmetry in the Univariate Lilliputian Model.- 5.5 Relative asymmetry.- 5.6 Appendix.- 6 Discretization and regularity.- 6.1 Introduction.- 6.2 Discretization framework.- 6.3 Optimal discretization for a given number of categories.- 6.4 Ideally regular concentration curves.- 6.5 On the determination of the number of categories.- 6.6 A parametric family of ideally regular Lilliputian curves.- 6.7 Appendix.- 7 Preliminary concepts of bivariate dependence.- 7.1Introduction.- 7.2 Contingency tables with m rows and k columns.- 7.3 Quadrant dependence.- 7.4 Matrices of ar’s for pairs of profilesTotal positivity of order two.- 7.5 The regression function.- 7.6 The monotone dependence function and the Gini Index.- 7.7 Appendix — Bibliographical remarks.- 8 Dependence Lilliputian Model.- 8.1 Introduction.- 8.2 Grade bivariate distributions and overrepresentation maps for probability tables.- 8.4 Spearman’s rho and Kendall’s tau expressed by volumes and masses in the unit cube.- 8.5 Grade regression functions and related measures.- 8.6 On permuting rows and columns of m × k probability tables.- 8.7 The hinged sequences of rows and columns.- 8.8 Appendix: Bibliographical remarks.- 9 Grade Correspondence Analysis and outlier detection.- 9.1 Introduction.- 9.2 Algorithms of GCA.- 9.3 Algorithm for Smooth Grade Correspondence Analysis (SGCA).- 9.4 Examples of GCA and SGCA results.- 9.5 Detection of rows and columns outlying the main trend.- 9.6 Appendix — Bibliographical remarks.- 10 Cluster analysis based on GCA.- 10.1 Introduction.- 10.2 Single and double grade clustering.- 10.3 Optimal grade clustering.- 10.4 Cluster analysis in the detection of mixtures.- 10.5 Cluster analysis and the detection of an imprecisely defined trend.- 10.6 On GCCA application to various data sets.- 10.7 Appendix.- 11 Regularity and the number of clusters.- 11.1 Introduction.- 11.2 Generalization of the parabola family from the .- 11.3 The ideal regularity of two-way data tables.- 11.4 Regularity and cluster detection.- 11.5 Cluster detection in finite data tables.- 11.6 Appendix — Bibliographical remarks.- 12 Grade approach to the analysis of finite data matrices.- 12.1 Introduction.- 12.2 Insight Examples.- 12.3Applicability of GCA.- 12.4 A revisit of the univariate data.- 12.5 Finite multivariate datasets and related inequality measures.- 12.6 Transformations of variables.- 12.7 Detection of outliers and decomposition of a dataset.- 13 Inequality measures for multivariate distributions.- 13.1 Introduction.- 13.2 Inequality measures for multivariate distributions with finite sets of records.- 13.3 Inequality measures for multivariate distributions with nonfinite sets of records.- 13.4 Inequality measures for continuous bivariate distributions.- 13.5 Inequality measures for grade multinormal distributions.- 13.6 Inequality measures for the Moran distributions.- 13.7 Appendix — link between grade similarity and dissimilarity of two regularly dependent random variables.- 14 Case studies with multivariate data.- 14.1 Introduction.- 14.2 Case Study 1 — Main Trend of Questionnaire Data.- 14.3 Case Study 1 — Decomposition of the dataset into regular subpopulations.- 14.4 Case Study 2 — Analysis of Engineering Data (Strength of Concrete).- 14.5 Case Study 2 — Decomposition of concrete mixtures into FITMT and OUT-MT.- 14.6 Final remarks for the two case studies.- 14.7 Appendix.- 15 The GradeStat program.- 15.1 Introduction.- 15.2 Main implemented features.- References.