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Applied Multivariate Statistical Analysis
Wolfgang Karl Härdle; Léopold Simar
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2012 Springer Berlin; Springer
Auflage: 3. Auflage
XVII, 516 Seiten
ISBN: 978-3-642-17229-8
€ 84,00
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Hauptbeschreibung
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 featuresA new Chapter on Regression Models has been addedAll numerical examples have been redone, updated and made reproducible in MATLAB or R, see www.quantlet.org for a repository of quantlets.
Kurztext / Annotation
Presenting the tools and concepts of multivariate data analysis in a way that is understandable for non-mathematicians, this third edition includes new features such as a chapter on regression models. All the numerical examples are reproduced in MATLAB or R.
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 featuresA new Chapter on Regression Models has been addedAll numerical examples have been redone, updated and made reproducible in MATLAB or R, see www.quantlet.org for a repository of quantlets.
Kurztext / Annotation
Presenting the tools and concepts of multivariate data analysis in a way that is understandable for non-mathematicians, this third edition includes new features such as a chapter on regression models. All the numerical examples are reproduced in MATLAB or R.
I. Descriptive Techniques: Comparison of Batches.- II. Multivariate Random Variables: A Short Excursion into Matrix Algebra.- Moving to Higher Dimensions.- Multivariate Distributions.- Theory of the Multinormal.- Theory of Estimation.- Hypothesis Testing.- III. Multivariate Techniques: Regression Models.- Decomposition of Data Matrices by Factors.- Principal Components Analysis.- Factor Analysis.- Cluster Analysis.- Discriminant Analysis.- Correspondence Analysis.- Canonical Correlation Analysis.- Multidimensional Scaling.- Conjoint Measurement Analysis.- Applications in Finance.- Computationally Intensive Techniques.- IV. Appendix.- Bibliography.- Index
Wolfgang Karl Härdle is Professor of Statistics at the Humboldt-Universität zu Berlin and the Director of CASE – the Centre for Applied Statistics and Economics. He teaches quantitative finance and semi-parametric statistical methods. His research focuses on dynamic factor models, multivariate statistics in finance and computational statistics. He is an elected member of the ISI and an advisor to the Guanghua School of Management, Peking University and to National Central University, Taiwan.Léopold Simar is Professor of Statistics at Université de Louvain, Louvain-la-Neuve, Belgium. He is teaching mathematical statistics, multivariate analysis, bootstrap methods in statistics and econometrics. His research focuses on non-parametric and semi-parametric methods and bootstrap techniques in statistics and econometrics. He is an elected member of the ISI and the past President of the Belgian Statistical Society.