Härdle, Wolfgang Karl; Simar, Léopold Applied multivariate statistical analysis. 5th revised and updated edition. (English) Zbl 1435.62005 Cham: Springer (ISBN 978-3-030-26005-7/pbk; 978-3-030-26006-4/ebook). xii, 558 p. (2019). This book has three main parts, an appendix and an index. Part I deals with descriptive techniques for multivariate data, e.g. histograms, scatterplots and kernel densities. Obviously, bivariate data are the easiest ones to be described on a two-dimensional sheet of paper (or screen). Part II deals with multivariate random variables, it starts with elementary matrix algebras, describes covariance and correlation, gives multivariate distributions, and ends with the theory of estimation and hypothesis testing. Part III, covering more than one half of the pages of this book, deals with multivariate techniques, its sections headlines read as follows: regression models, variable selection, decomposition of data matrices by factors, principal component analysis, factor analysis, cluster analysis, discriminant analysis, correspondence analysis, …application to finance, and computationally intensive techniques.For the 4th edition see [Zbl 1308.62002].Publisher’s description: “This textbook presents the tools and concepts used in multivariate data analysis in a style accessible for non-mathematicians and practitioners. All chapters include practical exercises that highlight applications in different multivariate data analysis fields, and all the examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. For this new edition, the book has been updated and extensively revised and now includes an extended chapter on cluster analysis. All solutions to the exercises are supplemented by R and MATLAB or SAS computer code and can be downloaded from the Quantlet platform. Practical exercises from this book and their solutions can also be found in the accompanying book [W. K. Härdle and Z. Hlávka, Multivariate statistics. Exercises and solutions. 2nd ed. Berlin: Springer (2015; Zbl 1323.00006)]. The Quantlet platform, quantlet.de, quantlet.com, quantlet.org, is an integrated QuantNet environment consisting of different types of statistics-related documents and program codes. Its goal is to promote reproducibility and offer a platform for sharing validated knowledge native to the social web. QuantNet and the corresponding data-driven document-based visualization allow readers to reproduce the tables, pictures and calculations presented in this Springer book.” From the introduction: “This fifth edition offers an extension of cluster analysis, including unsupervised learning and minimum spanning trees.” Reviewer: Hans-Jürgen Schmidt (Potsdam) Cited in 4 Documents MSC: 62-01 Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics 62H10 Multivariate distribution of statistics 62-04 Software, source code, etc. for problems pertaining to statistics 00A06 Mathematics for nonmathematicians (engineering, social sciences, etc.) 62H30 Classification and discrimination; cluster analysis (statistical aspects) 62R07 Statistical aspects of big data and data science Keywords:multivariate statistical analysis; cluster analysis; unsupervised learning; minimum spanning trees Citations:Zbl 1028.62039; Zbl 1115.62057; Zbl 1266.62032; Zbl 1308.62002; Zbl 1323.00006; Zbl 1183.62090 Software:Matlab; R; SAS; k-means++ PDFBibTeX XMLCite \textit{W. K. Härdle} and \textit{L. Simar}, Applied multivariate statistical analysis. 5th revised and updated edition. Cham: Springer (2019; Zbl 1435.62005) Full Text: DOI