Wolfgang Karl Härdle, Zdenek Hlávka

Multivariate Statistics: (eBook, PDF)

Exercises and Solutions

• Format: PDF

Produktbeschreibung

An ideal text for undergraduates in disciplines related to economics, this book could also help a university lecturer set up a modern multivariate analysis course. The authors have cleverly used exercises and their solutions to explore the concepts of multivariate data analysis. Broken down into three sections, this book has been structured to allow students in economics and finance to work their way through a well formulated exploration of this core topic. The book demonstrates the application of basic calculus and multivariate methods in real life situations. It contains more than 200 solved exercises, all of which are available in the R or XploRe languages. The corresponding libraries are downloadable from the Springer link web pages and from the author's home pages.

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Produktdetails

• Verlag: Springer US
• Seitenzahl: 368
• Erscheinungstermin: 5. Juli 2007
• Englisch
• ISBN-13: 9780387735085
• Artikelnr.: 43728716

Autorenporträt

Wolfgang Härdle, Humboldt-Universität zu Berlin, Germany / Zdenek Hlavka, Charles University in Prague, Czech Republic

Inhaltsangabe

Descriptive Techniques.- Comparison of Batches.- Multivariate Random Variables.- A Short Excursion into Matrix Algebra.- Moving to Higher Dimensions.- Multivariate Distributions.- Theory of the Multinormal.- Theory of Estimation.- Hypothesis Testing.- Multivariate Techniques.- Decomposition of Data Matrices by Factors.- Principal Component Analysis.- Factor Analysis.- Cluster Analysis.- Discriminant Analysis.- Correspondence Analysis.- Canonical Correlation Analysis.- Multidimensional Scaling.- Conjoint Measurement Analysis.- Applications in Finance.- Highly Interactive, Computationally Intensive Techniques.

Rezensionen

In general, I find this book particularly instructive, by discussing various techniques and analytical tools via exercises with rigorous solutions. The computer codes for computer-based exercises are available in R or XploRe languages through the Springer link web pages and from the authors home pages. The web links also provide access to real datasets used in the book. This is a very useful exercise book for students and instructors as well as for nonexperts using in applied multivariate data analysis. There has been large demand for techniques to handle and analyze high-dimensional data. In this regard, the book would be a good reference for researchers and students working in the theory or applications of multivariate statistical analysis. (Journal of the American Statistical Association, Dec. 2009, Vol. 104, No. 488)