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000856396 019__ $$a1066125391$$a1067238516$$a1078534214
000856396 020__ $$a9783319964225$$q(electronic book)
000856396 020__ $$a3319964224$$q(electronic book)
000856396 020__ $$z9783319964201
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000856396 0247_ $$a10.1007/978-3-319-96422-5$$2doi
000856396 035__ $$aSP(OCoLC)on1062394298
000856396 035__ $$aSP(OCoLC)1062394298$$z(OCoLC)1066125391$$z(OCoLC)1067238516$$z(OCoLC)1078534214
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000856396 049__ $$aISEA
000856396 050_4 $$aQA276
000856396 08204 $$a519.5$$223
000856396 1001_ $$aFilzmoser, Peter,$$eauthor.
000856396 24510 $$aApplied compositional data analysis :$$bwith worked examples in R /$$cPeter Filzmoser, Karel Hron, Matthias Templ.
000856396 264_1 $$aCham, Switzerland :$$bSpringer,$$c2018.
000856396 300__ $$a1 online resource (xvii, 280 pages) :$$billustrations
000856396 336__ $$atext$$btxt$$2rdacontent
000856396 337__ $$acomputer$$bc$$2rdamedia
000856396 338__ $$aonline resource$$bcr$$2rdacarrier
000856396 4901_ $$aSpringer series in statistics,$$x0172-7397
000856396 504__ $$aIncludes bibliographical references and index.
000856396 5050_ $$aIntro; Preface; Acknowledgments; Contents; Acronyms; 1 Compositional Data as a Methodological Concept; 1.1 What Are Compositional Data?; 1.2 Introductory Problems; 1.2.1 PhD Students Example; 1.2.2 Beer Data Example; 1.2.3 Geochemical Data Example; 1.3 Principles of Compositional Data Analysis; 1.4 Steps to a Concise Methodology; References; 2 Analyzing Compositional Data Using R; 2.1 Brief Overview on Packages Related to Compositional Data Analysis; 2.1.1 compositions; 2.1.2 robCompositions; 2.1.3 ggtern; 2.1.4 zCompositions; 2.1.5 mvoutlier, StatDA; 2.1.6 CoDaPack; 2.1.7 compositionsGUI
000856396 5058_ $$a2.2 The Statistics Environment R2.3 Basics in R; 2.3.1 Installation of R and Updates; 2.3.2 Install robCompositions; 2.3.3 Help; 2.3.4 The R Workspace and the Working Directory; 2.3.5 Data Types; 2.3.6 Generic Functions, Methods and Classes; References; 3 Geometrical Properties of Compositional Data; 3.1 Motivation; 3.2 Aitchison Geometry on the Simplex; 3.3 Coordinate Representations of Compositions; 3.3.1 Additive Logratio (alr) Coordinates; 3.3.2 Centered Logratio (clr) Coefficients; 3.3.3 Isometric Logratio (ilr) and Pivot Coordinates
000856396 5058_ $$a3.3.4 Special Coordinate Systems: Generalization of Pivot Coordinates3.3.5 Special Coordinate Systems: Symmetric Pivot Coordinates; 3.3.6 Special Coordinate Systems: Balances; 3.4 Examples; References; 4 Exploratory Data Analysis and Visualization; 4.1 Descriptive Statistics of Compositional Data; 4.2 Univariate Graphics; 4.3 Bivariate Plotting; 4.4 Multivariate Visualization; References; 5 First Steps for a Statistical Analysis; 5.1 Distributions and Statistical Inference; 5.1.1 Normality Testing; 5.1.2 Statistical Inference in Coordinates; 5.2 Classical and Robust Statistical Analysis
000856396 5058_ $$a5.2.1 Univariate Location5.2.2 Univariate Scale; 5.2.3 Multivariate Location and Covariance; 5.2.4 Center and Variation Matrix; 5.3 Outlier Detection; 5.3.1 Univariate Outliers; 5.3.2 Multivariate Outliers; 5.3.3 Interpretation of Multivariate Outliers; 5.4 Example; References; 6 Cluster Analysis; 6.1 Distance Measures and Dissimilarities; 6.2 Hierarchical Clustering Methods; 6.2.1 Agglomerative Clustering Algorithms; 6.2.1.1 Single Linkage; 6.2.1.2 Complete Linkage; 6.2.1.3 Average Linkage; 6.2.1.4 Ward's Method; 6.2.2 Tree Cutting; 6.3 Partitioning Methods; 6.4 Model-Based Clustering
000856396 5058_ $$a6.5 Fuzzy Clustering6.6 Clustering Parts: Q-Mode Clustering; 6.7 Evaluation; 6.8 Examples; References; 7 Principal Component Analysis; 7.1 Introductory Remarks; 7.2 Estimation of Principal Components; 7.2.1 Estimation by SVD; 7.2.2 Estimation by Decomposing the Covariance Matrix; 7.3 Compositional Biplot; 7.4 Examples; 7.4.1 Representation of Principal Components in a Ternary Diagram; 7.4.2 Example: Household Expenditures at EU Level; 7.4.3 Example: Beer Data; 7.4.4 Example with Two Different Compositions; 7.4.5 Example for PCA Including External Non-compositional Variables; References
000856396 506__ $$aAccess limited to authorized users.
000856396 520__ $$aThis book presents the statistical analysis of compositional data using the log-ratio approach. It includes a wide range of classical and robust statistical methods adapted for compositional data analysis, such as supervised and unsupervised methods like PCA, correlation analysis, classification and regression. In addition, it considers special data structures like high-dimensional compositions and compositional tables. The methodology introduced is also frequently compared to methods which ignore the specific nature of compositional data. It focuses on practical aspects of compositional data analysis rather than on detailed theoretical derivations, thus issues like graphical visualization and preprocessing (treatment of missing values, zeros, outliers and similar artifacts) form an important part of the book. Since it is primarily intended for researchers and students from applied fields like geochemistry, chemometrics, biology and natural sciences, economics, and social sciences, all the proposed methods are accompanied by worked-out examples in R using the package robCompositions.--$$cProvided by publisher.
000856396 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed November 12, 2018).
000856396 650_0 $$aMathematical statistics.
000856396 7001_ $$aHron, Karel,$$eauthor.
000856396 7001_ $$aTempl, Matthias,$$eauthor.
000856396 77608 $$iPrint version:$$z3319964208$$z9783319964201$$w(OCoLC)1041498517
000856396 830_0 $$aSpringer series in statistics,$$x0172-7397
000856396 852__ $$bebk
000856396 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-96422-5$$zOnline Access$$91397441.1
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