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Multivariate Statistics

Exercises and Solutions

  • Textbook
  • © 2015

Overview

  • Goes further than most similar textbooks by considering SIR techniques that are not found typically in multivariate textbooks
  • Data sets discussed in the book can be downloaded and analyzed by every statistical package
  • Contains hundreds of solved exercises

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Table of contents (20 chapters)

  1. Descriptive Techniques

  2. Multivariate Random Variables

  3. Multivariate Techniques

Keywords

About this book

The authors present tools and concepts of multivariate data analysis by means of exercises and their solutions. The first part is devoted to graphical techniques. The second part deals with multivariate random variables and presents the derivation of estimators and tests for various practical situations. The last part introduces a wide variety of exercises in applied multivariate data analysis. The book demonstrates the application of simple calculus and basic multivariate methods in real life situations. It contains altogether more than 250 solved exercises which can assist a university teacher in setting up a modern multivariate analysis course. All computer-based exercises are available in the R language. All data sets are included in the library SMSdata that may be downloaded via the quantlet download center www.quantlet.org. Data sets are available also via the Springer webpage. For interactive display of low-dimensional projections of a multivariate data set, we recommend GGobi.

Reviews

“The book basically contains a large number of exercises along with their solutions. … This book is a good source for researchers in the area of multivariate data analysis. It is also a good supplement to an advanced course on the subject. … this book takes a somewhat unique and different approach than a traditional textbook where one usually sees a topic covered in depth followed by a number of examples/exercises.” (Morteza Marzjarani, Technometrics, Vol. 58 (4), April, 2016) 

Authors and Affiliations

  • C.A.S.E. Centre f. Appl. Stat. & Econ. School of Business and Economics, Humboldt-Universität zu Berlin, Berlin, Germany

    Wolfgang Karl Härdle

  • Faculty of Mathematics and Physics, Department of Statistics, Charles University in Prague, Prague, Czech Republic

    Zdeněk Hlávka

About the authors

Wolfgang Karl Härdle is the Ladislaus von Bortkiewicz Professor of Statistics at the Humboldt-Universität zu Berlin and director of C.A.S.E. (Center for Applied Statistics and Economics), director of the CRC-649 (Collaborative Research Center) “Economic Risk” and director of the IRTG 1792 “High Dimensional Non-stationary Time Series”. He teaches quantitative finance and semi-parametric statistics.  His research focuses on dynamic factor models, multivariate statistics in finance and computational statistics. He is an elected member of the ISI (International Statistical Institute) and advisor to the Guanghua School of Management, Peking University, and senior fellow of Sim Kee Boon Institute of Financial Economics at the Singapore Management University.

Zdenek Hlávka studied mathematics at the Charles University in Prague and biostatistics at Limburgs Universitair Centrum in Diepenbeek. Later he held a position at Humboldt-Universität zu Berlin before he became amember of the Department of Probability and Mathematical Statistics at Charles University in Prague.

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