000			03562nam a22003377a 4500
001			saulb0010910
003			BD-SyAU
005			20260128122236.0
008			260128s2019 sz |||| o |||| 0|eng
010			_a 2019748527
020			_a9783030260057
040			_aDLC _beng _epn _erda _cDLC _dBD-SyAU
082	0	4	_a330.015195 _223 _bHAA
100	1		_aHärdle, Wolfgang Karl. _eauthor. _95266
245	1	0	_aApplied Multivariate Statistical Analysis / _cby Wolfgang Karl Härdle, Léopold Simar.
250			_a5th ed. 2015.
264		1	_aSwitzerland : _bSpringer, _c2019.
300			_axii, 558 p.: _bill.; _c25cm
504			_aIncludes bibliographical references and index.
505	0		_aI 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 -- Variable Selection -- 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: Symbols and Notations -- Data.
520			_aFocusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitioners. It surveys the basic principles and emphasizes both exploratory and inferential statistics; a new chapter on Variable Selection (Lasso, SCAD and Elastic Net) has also been added. All chapters include practical exercises that highlight applications in different multivariate data analysis fields: in quantitative financial studies, where the joint dynamics of assets are observed; in medicine, where recorded observations of subjects in different locations form the basis for reliable diagnoses and medication; and in quantitative marketing, where consumers' preferences are collected in order to construct models of consumer behavior. All of these examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. The fourth edition of this book on Applied Multivariate Statistical Analysis offers the following new features: A new chapter on Variable Selection (Lasso, SCAD and Elastic Net) All exercises are supplemented by R and MATLAB code that can be found on www.quantlet.de The practical exercises include solutions that can be found in Härdle, W. and Hlavka, Z., Multivariate Statistics: Exercises and Solutions. Springer Verlag, Heidelberg.
650		0	_aEconomic theory. _95267
650		0	_aEconomics, Mathematical. _9167
650		0	_aStatistics.
650	1	4	_aStatistics for Business, Management, Economics, Finance, Insurance. _0https://scigraph.springernature.com/ontologies/product-market-codes/S17010 _95268
650	2	4	_aEconomic Theory/Quantitative Economics/Mathematical Methods. _0https://scigraph.springernature.com/ontologies/product-market-codes/W29000 _95269
650	2	4	_aQuantitative Finance. _0https://scigraph.springernature.com/ontologies/product-market-codes/M13062 _95270
650	2	4	_aStatistical Theory and Methods. _0https://scigraph.springernature.com/ontologies/product-market-codes/S11001 _95271
700	1		_aSimar, Léopold. _eauthor. _95272
942			_2ddc _cBK
999			_c3844 _d3844