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024 7 $a10.1007/978-3-642-36005-3
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100   $a20150602d2015      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt
182   $cc
183   $acr
200 10$aMultivariate Statistics $eExercises and Solutions /$fby Wolfgang Karl Härdle, Zden?k Hlávka
205   $a2nd ed. 2015.
210  1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2015.
215   $a1 online resource (XXIV, 362 p. 123 illus., 30 illus. in color.) 
300   $aBibliographic Level Mode of Issuance: Monograph
311 08$a3-642-36004-1 
320   $aIncludes bibliographical references and index.
327   $aPart I Descriptive Techniques:  Comparison of Batches -- Part II Multivariate Random Variables:  A Short Excursion into Matrix Algebra -- Moving to Higher -- Multivariate -- Theory of the Multinormal --  Theory of Estimation -- Part III Multivariate Techniques: Regression Models -- Variable Selection -- 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 -- Data Sets -- References -- Index.
330   $aThe 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 R codes and data sets may be downloaded via the quantlet download center  www.quantlet.org or via the Springer webpage. For interactive display of low-dimensional projections of a multivariate data set, we recommend GGobi.
606   $aStatistics
606   $aMathematics$xData processing
606   $aInformation visualization
606   $aData mining
606   $aComputational intelligence
606   $aStatistical Theory and Methods
606   $aComputational Mathematics and Numerical Analysis
606   $aData and Information Visualization
606   $aData Mining and Knowledge Discovery
606   $aComputational Intelligence
615  0$aStatistics.
615  0$aMathematics$xData processing.
615  0$aInformation visualization.
615  0$aData mining.
615  0$aComputational intelligence.
615 14$aStatistical Theory and Methods.
615 24$aComputational Mathematics and Numerical Analysis.
615 24$aData and Information Visualization.
615 24$aData Mining and Knowledge Discovery.
615 24$aComputational Intelligence.
676   $a519.535
700   $aHärdle$b Wolfgang Karl$4aut$4http://id.loc.gov/vocabulary/relators/aut$0732741
702   $aHlávka$b Zden?k$4aut$4http://id.loc.gov/vocabulary/relators/aut
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910299774903321
996   $aMultivariate Statistics$92543291
997   $aUNINA