LEADER 04165nam  22006975  450 
001 9910300105603321
005 20250504233144.0
010   $a3-319-96422-4
024 7 $a10.1007/978-3-319-96422-5
035   $a(CKB)4100000007110555
035   $a(MiAaPQ)EBC5583548
035   $a(DE-He213)978-3-319-96422-5
035   $a(PPN)23247124X
035   $a(EXLCZ)994100000007110555
100   $a20181103d2018      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aApplied Compositional Data Analysis $eWith Worked Examples in R /$fby Peter Filzmoser, Karel Hron, Matthias Templ
205   $a1st ed. 2018.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018.
215   $a1 online resource (288 pages)
225 1 $aSpringer Series in Statistics,$x2197-568X
311 08$a3-319-96420-8 
327   $aPreface -- Acknowledgements -- Compositional data as a methodological concept -- Analyzing compositional data using R -- Geometrical properties of compositional data -- Exploratory data analysis and visualization -- First steps for a statistical analysis -- Cluster analysis -- Principal component analysis -- Correlation analysis -- Discriminant analysis -- Regression analysis -- Methods for high-dimensional compositional data -- Compositional tables -- Preprocessing issues -- Index.-.
330   $aThis book presents the statistical analysis of compositional data using the log-ratio approach. It includes a wide range of classical and robust statistical methods adapted for compositional data analysis, such as supervised and unsupervised methods like PCA, correlation analysis, classification and regression. In addition, it considers special data structures like high-dimensional compositions and compositional tables. The methodology introduced is also frequently compared to methods which ignore the specific nature of compositional data. It focuses on practical aspects of compositional data analysis rather than on detailed theoretical derivations, thus issues like graphical visualization and preprocessing (treatment of missing values, zeros, outliers and similar artifacts) form an important part of the book. Since it is primarily intended for researchers and students from applied fields like geochemistry, chemometrics, biology and natural sciences, economics, and social sciences, all the proposed methods are accompanied by worked-out examples in R using the package robCompositions.
410  0$aSpringer Series in Statistics,$x2197-568X
606   $aStatistics
606   $aMathematical statistics$xData processing
606   $aStatistics
606   $aGeochemistry
606   $aBiometry
606   $aSocial sciences$xStatistical methods
606   $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
606   $aStatistics and Computing
606   $aStatistical Theory and Methods
606   $aGeochemistry
606   $aBiostatistics
606   $aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy
615  0$aStatistics.
615  0$aMathematical statistics$xData processing.
615  0$aStatistics.
615  0$aGeochemistry.
615  0$aBiometry.
615  0$aSocial sciences$xStatistical methods.
615 14$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
615 24$aStatistics and Computing.
615 24$aStatistical Theory and Methods.
615 24$aGeochemistry.
615 24$aBiostatistics.
615 24$aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy.
676   $a519.5
700   $aFilzmoser$b Peter$4aut$4http://id.loc.gov/vocabulary/relators/aut$0721256
702   $aHron$b Karel$4aut$4http://id.loc.gov/vocabulary/relators/aut
702   $aTempl$b Matthias$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910300105603321
996   $aApplied Compositional Data Analysis$92182076
997   $aUNINA