國立虎尾科技大學 |

Applied compositional data analysis = with worked examples in R /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Applied compositional data analysis/ by Peter Filzmoser, Karel Hron, Matthias Templ.
Reminder of title:	with worked examples in R /
Author:	Filzmoser, Peter.
other author:	Hron, Karel.
Published:	Cham :Springer International Publishing : : 2018.,
Description:	xvii, 280 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Mathematical statistics. -
Online resource:	https://doi.org/10.1007/978-3-319-96422-5
ISBN:	9783319964225

Applied compositional data analysis = with worked examples in R /
Filzmoser, Peter.

Applied compositional data analysiswith worked examples in R /[electronic resource] :by Peter Filzmoser, Karel Hron, Matthias Templ. - Cham :Springer International Publishing :2018. - xvii, 280 p. :ill. (some col.), digital ;24 cm. - Springer series in statistics,0172-7397. - Springer series in statistics..

Preface -- 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.

This 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.

ISBN: 9783319964225

Standard No.: 10.1007/978-3-319-96422-5doiSubjects--Topical Terms:

527941
Mathematical statistics.

LC Class. No.: QA276 / .F559 2018

Dewey Class. No.: 519.5

Applied compositional data analysis = with worked examples in R /
LDR:02664nam a2200337 a 4500 001 929791
003 DE-He213
005 20190326171607.0
006 m d
007 cr nn 008maaau
008 190626s2018 gw s 0 eng d
020 $a 9783319964225 $q (electronic bk.)
020 $a 9783319964201 $q (paper)
024 7 $a 10.1007/978-3-319-96422-5 $2 doi
035 $a 978-3-319-96422-5
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276 $b .F559 2018
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 519.5 $2 23
090 $a QA276 $b .F489 2018
100 1 $a Filzmoser, Peter. $3 867298
245 1 0 $a Applied compositional data analysis $h [electronic resource] : $b with worked examples in R / $c by Peter Filzmoser, Karel Hron, Matthias Templ.
260 $a Cham : $c 2018. $b Springer International Publishing : $b Imprint: Springer,
300 $a xvii, 280 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Springer series in statistics, $x 0172-7397
505 0 $a Preface -- 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.
520 $a This 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.
650 0 $a Mathematical statistics. $3 527941
650 1 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 782247
650 2 4 $a Statistics and Computing/Statistics Programs. $3 669775
650 2 4 $a Statistical Theory and Methods. $3 671396
650 2 4 $a Geochemistry. $3 648291
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 670172
650 2 4 $a Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. $3 670129
700 1 $a Hron, Karel. $3 1210568
700 1 $a Templ, Matthias. $3 1142753
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
830 0 $a Springer series in statistics. $3 882180
856 4 0 $u https://doi.org/10.1007/978-3-319-96422-5
950 $a Mathematics and Statistics (Springer-11649)