The Resource Applied multivariate statistical analysis, Wolfgang Karl Härdle, Léopold Simar
Applied multivariate statistical analysis, Wolfgang Karl Härdle, Léopold Simar
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The item Applied multivariate statistical analysis, Wolfgang Karl Härdle, Léopold Simar represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.This item is available to borrow from 2 library branches.
Resource Information
The item Applied multivariate statistical analysis, Wolfgang Karl Härdle, Léopold Simar represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.
This item is available to borrow from 2 library branches.
 Summary
 Most of the observable phenomena in the empirical sciences are of a multivariate nature.¡ In financial studies, assets are observed simultaneously and their joint development is analysed to better understand general risk and to track indices.¡ In medicine recorded observations of subjects in different locations are the basis of reliable diagnoses and medication.¡ In quantitative marketing consumer preferences are collected in order to construct models of consumer behavior.¡ The underlying data structure of these and many other quantitative studies of applied sciences is multivariate.¡ Focusing on applications this book presents the tools and concepts of multivariate data analysis in a way that is understandable for nonmathematicians 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. The third edition of this book on Applied Multivariate Statistical Analysis offers the following new features A new Chapter on Regression Models has been added All numerical examples have been redone, updated and made reproducible in MATLAB or R, see www.quantlet.org for a repository of quantlets
 Language
 eng
 Edition
 3rd ed.
 Extent
 1 online resource (xvii, 516 pages)
 Contents

 Applied Multivariate Statistical Analysis; Preface to the 3rd Edition; Preface to the 2nd Edition; Preface to the 1st Edition; Contents; Part I: Descriptive Techniques; Chapter 1: Comparison of Batches; 1.1 Boxplots; 1.2 Histograms; 1.3 Kernel Densities; 1.4 Scatterplots; 1.5 ChernoffFlury Faces; 1.6 Andrews' Curves; 1.7 Parallel Coordinate Plots; 1.8 Hexagon Plots; 1.9 Boston Housing; 1.10 Exercises; Part II: Multivariate Random Variables; Chapter 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 Matrices2.6 Geometrical Aspects; 2.7 Exercises; Chapter 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; Chapter 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 HeavyTailed Distributions; 4.7 Copulae; 4.8 Bootstrap; 4.9 Exercises
 Chapter 5: Theory of the Multinormal5.1 Elementary Properties of the Multinormal; 5.2 The Wishart Distribution; 5.3 Hotelling's T2Distribution; 5.4 Spherical and Elliptical Distributions; 5.5 Exercises; Chapter 6: Theory of Estimation; 6.1 The Likelihood Function; 6.2 The CramerRao Lower Bound; 6.3 Exercises; Chapter 7: Hypothesis Testing; 7.1 Likelihood Ratio Test; 7.2 Linear Hypothesis; 7.3 Boston Housing; 7.4 Exercises; Part III: Multivariate Techniques; Chapter 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 Responses8.2.1 Multinomial Sampling and Contingency Tables; 8.2.2 Loglinear Models for Contingency Tables; 8.2.3 Testing Issues with Count Data; 8.2.4 Logit Models; 8.3 Exercises; Chapter 9: Decomposition of Data Matrices by Factors; 9.1 The Geometric Point of View; 9.2 Fitting the pdimensional Point Cloud; 9.3 Fitting the ndimensional Point Cloud; 9.4 Relations Between Subspaces; 9.5 Practical Computation; 9.6 Exercises; Chapter 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 PCs10.5 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; Chapter 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; Chapter 12: Cluster Analysis; 12.1 The Problem; 12.2 The Proximity Between Objects; 12.3 Cluster Algorithms; 12.4 Boston Housing; 12.5 Exercises; Chapter 13: Discriminant Analysis
 Isbn
 9783642172281
 Label
 Applied multivariate statistical analysis
 Title
 Applied multivariate statistical analysis
 Statement of responsibility
 Wolfgang Karl Härdle, Léopold Simar
 Subject

 Economics
 Finance
 Mathematical statistics
 Multivariate Analysis
 Multivariate analysis
 Multivariate analysis
 Multivariate analysis
 Quantitative Finance
 Statistical Theory and Methods
 Statistics
 Statistics
 Statistics
 Statistics as Topic
 Statistics for Business/Economics/Mathematical Finance/Insurance
 Economic Theory
 Language
 eng
 Summary
 Most of the observable phenomena in the empirical sciences are of a multivariate nature.¡ In financial studies, assets are observed simultaneously and their joint development is analysed to better understand general risk and to track indices.¡ In medicine recorded observations of subjects in different locations are the basis of reliable diagnoses and medication.¡ In quantitative marketing consumer preferences are collected in order to construct models of consumer behavior.¡ The underlying data structure of these and many other quantitative studies of applied sciences is multivariate.¡ Focusing on applications this book presents the tools and concepts of multivariate data analysis in a way that is understandable for nonmathematicians 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. The third edition of this book on Applied Multivariate Statistical Analysis offers the following new features A new Chapter on Regression Models has been added All numerical examples have been redone, updated and made reproducible in MATLAB or R, see www.quantlet.org for a repository of quantlets
 Cataloging source
 GW5XE
 http://library.link/vocab/creatorName
 Härdle, Wolfgang
 Dewey number
 519.5/35
 Index
 index present
 LC call number
 QA278
 LC item number
 .H37 2012
 Literary form
 non fiction
 Nature of contents

 dictionaries
 bibliography
 NLM call number
 Online Book
 http://library.link/vocab/relatedWorkOrContributorName
 Simar, Léopold
 http://library.link/vocab/subjectName

 Multivariate analysis
 Multivariate Analysis
 Statistics as Topic
 Multivariate analysis
 Label
 Applied multivariate statistical analysis, Wolfgang Karl Härdle, Léopold Simar
 Antecedent source
 unknown
 Bibliography note
 Includes bibliographical references and index
 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

 Applied Multivariate Statistical Analysis; Preface to the 3rd Edition; Preface to the 2nd Edition; Preface to the 1st Edition; Contents; Part I: Descriptive Techniques; Chapter 1: Comparison of Batches; 1.1 Boxplots; 1.2 Histograms; 1.3 Kernel Densities; 1.4 Scatterplots; 1.5 ChernoffFlury Faces; 1.6 Andrews' Curves; 1.7 Parallel Coordinate Plots; 1.8 Hexagon Plots; 1.9 Boston Housing; 1.10 Exercises; Part II: Multivariate Random Variables; Chapter 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 Matrices2.6 Geometrical Aspects; 2.7 Exercises; Chapter 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; Chapter 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 HeavyTailed Distributions; 4.7 Copulae; 4.8 Bootstrap; 4.9 Exercises
 Chapter 5: Theory of the Multinormal5.1 Elementary Properties of the Multinormal; 5.2 The Wishart Distribution; 5.3 Hotelling's T2Distribution; 5.4 Spherical and Elliptical Distributions; 5.5 Exercises; Chapter 6: Theory of Estimation; 6.1 The Likelihood Function; 6.2 The CramerRao Lower Bound; 6.3 Exercises; Chapter 7: Hypothesis Testing; 7.1 Likelihood Ratio Test; 7.2 Linear Hypothesis; 7.3 Boston Housing; 7.4 Exercises; Part III: Multivariate Techniques; Chapter 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 Responses8.2.1 Multinomial Sampling and Contingency Tables; 8.2.2 Loglinear Models for Contingency Tables; 8.2.3 Testing Issues with Count Data; 8.2.4 Logit Models; 8.3 Exercises; Chapter 9: Decomposition of Data Matrices by Factors; 9.1 The Geometric Point of View; 9.2 Fitting the pdimensional Point Cloud; 9.3 Fitting the ndimensional Point Cloud; 9.4 Relations Between Subspaces; 9.5 Practical Computation; 9.6 Exercises; Chapter 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 PCs10.5 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; Chapter 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; Chapter 12: Cluster Analysis; 12.1 The Problem; 12.2 The Proximity Between Objects; 12.3 Cluster Algorithms; 12.4 Boston Housing; 12.5 Exercises; Chapter 13: Discriminant Analysis
 Control code
 772518705
 Dimensions
 unknown
 Edition
 3rd ed.
 Extent
 1 online resource (xvii, 516 pages)
 File format
 unknown
 Form of item
 online
 Isbn
 9783642172281
 Level of compression
 unknown
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Other control number
 10.1007/9783642172298
 Quality assurance targets
 not applicable
 Reformatting quality
 unknown
 Sound
 unknown sound
 Specific material designation
 remote
 System control number
 (OCoLC)772518705
 Label
 Applied multivariate statistical analysis, Wolfgang Karl Härdle, Léopold Simar
 Antecedent source
 unknown
 Bibliography note
 Includes bibliographical references and index
 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

 Applied Multivariate Statistical Analysis; Preface to the 3rd Edition; Preface to the 2nd Edition; Preface to the 1st Edition; Contents; Part I: Descriptive Techniques; Chapter 1: Comparison of Batches; 1.1 Boxplots; 1.2 Histograms; 1.3 Kernel Densities; 1.4 Scatterplots; 1.5 ChernoffFlury Faces; 1.6 Andrews' Curves; 1.7 Parallel Coordinate Plots; 1.8 Hexagon Plots; 1.9 Boston Housing; 1.10 Exercises; Part II: Multivariate Random Variables; Chapter 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 Matrices2.6 Geometrical Aspects; 2.7 Exercises; Chapter 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; Chapter 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 HeavyTailed Distributions; 4.7 Copulae; 4.8 Bootstrap; 4.9 Exercises
 Chapter 5: Theory of the Multinormal5.1 Elementary Properties of the Multinormal; 5.2 The Wishart Distribution; 5.3 Hotelling's T2Distribution; 5.4 Spherical and Elliptical Distributions; 5.5 Exercises; Chapter 6: Theory of Estimation; 6.1 The Likelihood Function; 6.2 The CramerRao Lower Bound; 6.3 Exercises; Chapter 7: Hypothesis Testing; 7.1 Likelihood Ratio Test; 7.2 Linear Hypothesis; 7.3 Boston Housing; 7.4 Exercises; Part III: Multivariate Techniques; Chapter 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 Responses8.2.1 Multinomial Sampling and Contingency Tables; 8.2.2 Loglinear Models for Contingency Tables; 8.2.3 Testing Issues with Count Data; 8.2.4 Logit Models; 8.3 Exercises; Chapter 9: Decomposition of Data Matrices by Factors; 9.1 The Geometric Point of View; 9.2 Fitting the pdimensional Point Cloud; 9.3 Fitting the ndimensional Point Cloud; 9.4 Relations Between Subspaces; 9.5 Practical Computation; 9.6 Exercises; Chapter 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 PCs10.5 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; Chapter 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; Chapter 12: Cluster Analysis; 12.1 The Problem; 12.2 The Proximity Between Objects; 12.3 Cluster Algorithms; 12.4 Boston Housing; 12.5 Exercises; Chapter 13: Discriminant Analysis
 Control code
 772518705
 Dimensions
 unknown
 Edition
 3rd ed.
 Extent
 1 online resource (xvii, 516 pages)
 File format
 unknown
 Form of item
 online
 Isbn
 9783642172281
 Level of compression
 unknown
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Other control number
 10.1007/9783642172298
 Quality assurance targets
 not applicable
 Reformatting quality
 unknown
 Sound
 unknown sound
 Specific material designation
 remote
 System control number
 (OCoLC)772518705
Subject
 Economics
 Finance
 Mathematical statistics
 Multivariate Analysis
 Multivariate analysis
 Multivariate analysis
 Multivariate analysis
 Quantitative Finance
 Statistical Theory and Methods
 Statistics
 Statistics
 Statistics
 Statistics as Topic
 Statistics for Business/Economics/Mathematical Finance/Insurance
 Economic Theory
Genre
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