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The Resource Applied multivariate statistical analysis, Wolfgang Karl Härdle, Léopold Simar

Applied multivariate statistical analysis, Wolfgang Karl Härdle, Léopold Simar

Label
Applied multivariate statistical analysis
Title
Applied multivariate statistical analysis
Statement of responsibility
Wolfgang Karl Härdle, Léopold Simar
Creator
Contributor
Subject
Language
eng
Summary
"Focusing on applications this book presents the tools and concepts of multivariate data analysis in a way that is understandable for non-mathematicians and practitioners who need to analyze statistical data. The book surveys the basic principles of multivariate statistical data analysis and emphasizes both exploratory and inferential statistics. All chapters have exercises that highlight applications in different fields." -- Back cover
Cataloging source
DLC
http://library.link/vocab/creatorName
Härdle, Wolfgang
Dewey number
519.5/35
Illustrations
illustrations
Index
index present
LC call number
QA278
LC item number
.H346 2012
Literary form
non fiction
Nature of contents
bibliography
http://library.link/vocab/relatedWorkOrContributorName
Simar, Léopold
http://library.link/vocab/subjectName
Multivariate analysis
Label
Applied multivariate statistical analysis, Wolfgang Karl Härdle, Léopold Simar
Instantiates
Publication
Bibliography note
Includes bibliographical references (pages 509-512) and index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • pt. I Descriptive Techniques -- 1.Comparison of Batches -- 1.1.Boxplots -- 1.2.Histograms -- 1.3.Kernel Densities -- 1.4.Scatterplots -- 1.5.Chernoff-Flury Faces -- 1.6.Andrews' Curves -- 1.7.Parallel Coordinate Plots -- 1.8.Hexagon Plots -- 1.9.Boston Housing -- 1.10.Exercises -- pt. II Multivariate Random Variables -- 2.A Short Excursion into Matrix Algebra -- 2.1.Elementary Operations -- 2.2.Spectral Decompositions -- 2.3.Quadratic Forms -- 2.4.Derivatives -- 2.5.Partitioned Matrices -- 2.6.Geometrical Aspects -- 2.7.Exercises -- 3.Moving to Higher Dimensions -- 3.1.Covariance -- 3.2.Correlation -- 3.3.Summary Statistics -- 3.4.Linear Model for Two Variables -- 3.5.Simple Analysis of Variance -- 3.6.Multiple Linear Model -- 3.7.Boston Housing -- 3.8.Exercises -- 4.Multivariate Distributions -- 4.1.Distribution and Density Function -- 4.2.Moments and Characteristic Functions -- 4.3.Transformations -- 4.4.The Multinormal Distribution --
  • 4.5.Sampling Distributions and Limit Theorems -- 4.6.Heavy-Tailed Distributions -- 4.7.Copulae -- 4.8.Bootstrap -- 4.9.Exercises -- 5.Theory of the Multinormal -- 5.1.Elementary Properties of the Multinormal -- 5.2.The Wishart Distribution -- 5.3.Hotelling's T2-Distribution -- 5.4.Spherical and Elliptical Distributions -- 5.5.Exercises -- 6.Theory of Estimation -- 6.1.The Likelihood Function -- 6.2.The Cramer-Rao Lower Bound -- 6.3.Exercises -- 7.Hypothesis Testing -- 7.1.Likelihood Ratio Test -- 7.2.Linear Hypothesis -- 7.3.Boston Housing -- 7.4.Exercises -- pt. III Multivariate Techniques -- 8.Regression Models -- 8.1.General ANOVA and ANCOVA Models -- 8.1.1.ANOVA Models -- 8.1.2.ANCOVA Models -- 8.1.3.Boston Housing -- 8.2.Categorical Responses -- 8.2.1.Multinomial Sampling and Contingency Tables -- 8.2.2.Log-linear Models for Contingency Tables -- 8.2.3.Testing Issues with Count Data -- 8.2.4.Logit Models -- 8.3.Exercises --
  • 9.Decomposition of Data Matrices by Factors -- 9.1.The Geometric Point of View -- 9.2.Fitting the p-dimensional Point Cloud -- 9.3.Fitting the n-dimensional Point Cloud -- 9.4.Relations Between Subspaces -- 9.5.Practical Computation -- 9.6.Exercises -- 10.Principal Components Analysis -- 10.1.Standardized Linear Combination -- 10.2.Principal Components in Practice -- 10.3.Interpretation of the PCs -- 10.4.Asymptotic Properties of the PCs -- 10.5.Normalized Principal Components Analysis -- 10.6.Principal Components as a Factorial Method -- 10.7.Common Principal Components -- 10.8.Boston Housing -- 10.9.More Examples -- 10.10.Exercises -- 11.Factor Analysis -- 11.1.The Orthogonal Factor Model -- 11.2.Estimation of the Factor Model -- 11.3.Factor Scores and Strategies -- 11.4.Boston Housing -- 11.5.Exercises -- 12.Cluster Analysis -- 12.1.The Problem -- 12.2.The Proximity Between Objects -- 12.3.Cluster Algorithms -- 12.4.Boston Housing -- 12.5.Exercises --
  • 13.Discriminant Analysis -- 13.1.Allocation Rules for Known Distributions -- 13.2.Discrimination Rules in Practice -- 13.3.Boston Housing -- 13.4.Exercises -- 14.Correspondence Analysis -- 14.1.Motivation -- 14.2.Chi-square Decomposition -- 14.3.Correspondence Analysis in Practice -- 14.4.Exercises -- 15.Canonical Correlation Analysis -- 15.1.Most Interesting Linear Combination -- 15.2.Canonical Correlation in Practice -- 15.3.Exercises -- 16.Multidimensional Scaling -- 16.1.The Problem -- 16.2.Metric Multidimensional Scaling -- 16.3.Nonmetric Multidimensional Scaling -- 16.4.Exercises -- 17.Conjoint Measurement Analysis -- 17.1.Introduction -- 17.2.Design of Data Generation -- 17.3.Estimation of Preference Orderings -- 17.4.Exercises -- 18.Applications in Finance -- 18.1.Portfolio Choice -- 18.2.Efficient Portfolio -- 18.3.Efficient Portfolios in Practice -- 18.4.The Capital Pricing Model (CAPM) -- 18.5.Exercises --
  • 19.Computationally Intensive Techniques -- 19.1.Simplicial Depth -- 19.2.Projection Pursuit -- 19.3.Sliced Inverse Regression -- 19.4.Support Vector Machines -- 19.5.Classification and Regression Trees -- 19.6.Boston Housing -- 19.7.Exercises -- pt. IV Appendix -- Appendix A Symbols and Notations -- Appendix B Data -- B.1.Boston Housing Data -- B.2.Swiss Bank Notes -- B.3.Car Data -- B.4.Classic Blue Pullovers Data -- B.5.U.S. Companies Data -- B.6.French Food Data -- B.7.Car Marks -- B.8.French Baccalaureat Frequencies -- B.9.Journaux Data -- B.10.U.S. Crime Data -- B.11.Plasma Data -- B.12.WAIS Data -- B.13.ANOVA Data -- B.14.Timebudget Data -- B.15.Geopol Data -- B.16.U.S. Health Data -- B.17.Vocabulary Data -- B.18.Athletic Records Data -- B.19.Unemployment Data -- B.20.Annual Population Data -- B.21.Bankruptcy Data I -- B.22.Bankruptcy Data II
Control code
782052909
Dimensions
24 cm
Edition
3rd ed.
Extent
xvii, 516 pages
Isbn
9783642172281
Isbn Type
(acid-free paper)
Lccn
2011944029
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
illustrations (some color)
System control number
(OCoLC)782052909
Label
Applied multivariate statistical analysis, Wolfgang Karl Härdle, Léopold Simar
Publication
Bibliography note
Includes bibliographical references (pages 509-512) and index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • pt. I Descriptive Techniques -- 1.Comparison of Batches -- 1.1.Boxplots -- 1.2.Histograms -- 1.3.Kernel Densities -- 1.4.Scatterplots -- 1.5.Chernoff-Flury Faces -- 1.6.Andrews' Curves -- 1.7.Parallel Coordinate Plots -- 1.8.Hexagon Plots -- 1.9.Boston Housing -- 1.10.Exercises -- pt. II Multivariate Random Variables -- 2.A Short Excursion into Matrix Algebra -- 2.1.Elementary Operations -- 2.2.Spectral Decompositions -- 2.3.Quadratic Forms -- 2.4.Derivatives -- 2.5.Partitioned Matrices -- 2.6.Geometrical Aspects -- 2.7.Exercises -- 3.Moving to Higher Dimensions -- 3.1.Covariance -- 3.2.Correlation -- 3.3.Summary Statistics -- 3.4.Linear Model for Two Variables -- 3.5.Simple Analysis of Variance -- 3.6.Multiple Linear Model -- 3.7.Boston Housing -- 3.8.Exercises -- 4.Multivariate Distributions -- 4.1.Distribution and Density Function -- 4.2.Moments and Characteristic Functions -- 4.3.Transformations -- 4.4.The Multinormal Distribution --
  • 4.5.Sampling Distributions and Limit Theorems -- 4.6.Heavy-Tailed Distributions -- 4.7.Copulae -- 4.8.Bootstrap -- 4.9.Exercises -- 5.Theory of the Multinormal -- 5.1.Elementary Properties of the Multinormal -- 5.2.The Wishart Distribution -- 5.3.Hotelling's T2-Distribution -- 5.4.Spherical and Elliptical Distributions -- 5.5.Exercises -- 6.Theory of Estimation -- 6.1.The Likelihood Function -- 6.2.The Cramer-Rao Lower Bound -- 6.3.Exercises -- 7.Hypothesis Testing -- 7.1.Likelihood Ratio Test -- 7.2.Linear Hypothesis -- 7.3.Boston Housing -- 7.4.Exercises -- pt. III Multivariate Techniques -- 8.Regression Models -- 8.1.General ANOVA and ANCOVA Models -- 8.1.1.ANOVA Models -- 8.1.2.ANCOVA Models -- 8.1.3.Boston Housing -- 8.2.Categorical Responses -- 8.2.1.Multinomial Sampling and Contingency Tables -- 8.2.2.Log-linear Models for Contingency Tables -- 8.2.3.Testing Issues with Count Data -- 8.2.4.Logit Models -- 8.3.Exercises --
  • 9.Decomposition of Data Matrices by Factors -- 9.1.The Geometric Point of View -- 9.2.Fitting the p-dimensional Point Cloud -- 9.3.Fitting the n-dimensional Point Cloud -- 9.4.Relations Between Subspaces -- 9.5.Practical Computation -- 9.6.Exercises -- 10.Principal Components Analysis -- 10.1.Standardized Linear Combination -- 10.2.Principal Components in Practice -- 10.3.Interpretation of the PCs -- 10.4.Asymptotic Properties of the PCs -- 10.5.Normalized Principal Components Analysis -- 10.6.Principal Components as a Factorial Method -- 10.7.Common Principal Components -- 10.8.Boston Housing -- 10.9.More Examples -- 10.10.Exercises -- 11.Factor Analysis -- 11.1.The Orthogonal Factor Model -- 11.2.Estimation of the Factor Model -- 11.3.Factor Scores and Strategies -- 11.4.Boston Housing -- 11.5.Exercises -- 12.Cluster Analysis -- 12.1.The Problem -- 12.2.The Proximity Between Objects -- 12.3.Cluster Algorithms -- 12.4.Boston Housing -- 12.5.Exercises --
  • 13.Discriminant Analysis -- 13.1.Allocation Rules for Known Distributions -- 13.2.Discrimination Rules in Practice -- 13.3.Boston Housing -- 13.4.Exercises -- 14.Correspondence Analysis -- 14.1.Motivation -- 14.2.Chi-square Decomposition -- 14.3.Correspondence Analysis in Practice -- 14.4.Exercises -- 15.Canonical Correlation Analysis -- 15.1.Most Interesting Linear Combination -- 15.2.Canonical Correlation in Practice -- 15.3.Exercises -- 16.Multidimensional Scaling -- 16.1.The Problem -- 16.2.Metric Multidimensional Scaling -- 16.3.Nonmetric Multidimensional Scaling -- 16.4.Exercises -- 17.Conjoint Measurement Analysis -- 17.1.Introduction -- 17.2.Design of Data Generation -- 17.3.Estimation of Preference Orderings -- 17.4.Exercises -- 18.Applications in Finance -- 18.1.Portfolio Choice -- 18.2.Efficient Portfolio -- 18.3.Efficient Portfolios in Practice -- 18.4.The Capital Pricing Model (CAPM) -- 18.5.Exercises --
  • 19.Computationally Intensive Techniques -- 19.1.Simplicial Depth -- 19.2.Projection Pursuit -- 19.3.Sliced Inverse Regression -- 19.4.Support Vector Machines -- 19.5.Classification and Regression Trees -- 19.6.Boston Housing -- 19.7.Exercises -- pt. IV Appendix -- Appendix A Symbols and Notations -- Appendix B Data -- B.1.Boston Housing Data -- B.2.Swiss Bank Notes -- B.3.Car Data -- B.4.Classic Blue Pullovers Data -- B.5.U.S. Companies Data -- B.6.French Food Data -- B.7.Car Marks -- B.8.French Baccalaureat Frequencies -- B.9.Journaux Data -- B.10.U.S. Crime Data -- B.11.Plasma Data -- B.12.WAIS Data -- B.13.ANOVA Data -- B.14.Timebudget Data -- B.15.Geopol Data -- B.16.U.S. Health Data -- B.17.Vocabulary Data -- B.18.Athletic Records Data -- B.19.Unemployment Data -- B.20.Annual Population Data -- B.21.Bankruptcy Data I -- B.22.Bankruptcy Data II
Control code
782052909
Dimensions
24 cm
Edition
3rd ed.
Extent
xvii, 516 pages
Isbn
9783642172281
Isbn Type
(acid-free paper)
Lccn
2011944029
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
illustrations (some color)
System control number
(OCoLC)782052909

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