The Resource Applied multivariate statistical analysis, Wolfgang Karl Härdle, Léopold Simar
Applied multivariate statistical analysis, Wolfgang Karl Härdle, Léopold Simar
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.
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 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. 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 Chernoff-Flury 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 Heavy-Tailed 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 T2-Distribution; 5.4 Spherical and Elliptical Distributions; 5.5 Exercises; Chapter 6: Theory of Estimation; 6.1 The Likelihood Function; 6.2 The Cramer-Rao 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 Log-linear 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 p-dimensional Point Cloud; 9.3 Fitting the n-dimensional 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 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. 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 Chernoff-Flury 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 Heavy-Tailed 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 T2-Distribution; 5.4 Spherical and Elliptical Distributions; 5.5 Exercises; Chapter 6: Theory of Estimation; 6.1 The Likelihood Function; 6.2 The Cramer-Rao 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 Log-linear 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 p-dimensional Point Cloud; 9.3 Fitting the n-dimensional 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
- 10.1007/978-3-642-17229-8
- 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 Chernoff-Flury 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 Heavy-Tailed 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 T2-Distribution; 5.4 Spherical and Elliptical Distributions; 5.5 Exercises; Chapter 6: Theory of Estimation; 6.1 The Likelihood Function; 6.2 The Cramer-Rao 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 Log-linear 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 p-dimensional Point Cloud; 9.3 Fitting the n-dimensional 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
- 10.1007/978-3-642-17229-8
- 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
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