Applied Compositional Data Analysis, 1st ed. 2018 With Worked Examples in R Springer Series in Statistics Series
Auteurs : Filzmoser Peter, Hron Karel, Templ Matthias
Peter Filzmoser is a Professor of Statistics at the Vienna University of Technology, Austria. He received his Ph.D. and postdoctoral lecture qualification from the same university. He was a Visiting Professor at Toulouse, France and Belarus. Furthermore, he has authored more than 200 research articles and several R packages and is a co-author of a book on multivariate methods in chemometrics (CRC Press, 2009) and on analyzing environmental data (Wiley, 2008).
Karel Hron is an Associate Professor at Palacký University in Olomouc, Czech Republic. He holds a Ph.D. in applied mathematics and is active in promoting his discipline. His research activities focus on statistical analysis of compositional data and multivariate statistical analysis in general. His methods and algorithms are implemented in the statistical software R. He primarily collaborates with researchers from chemometrics and environmental sciences.
Matthias Templ is alecturer at the Zurich University of Applied Sciences, Switzerland. His main research interests include computational statistics, statistical modeling and official statistics. He is author of several R packages, such as the R package sdcMicro for statistical disclosure control, the simPop package for simulation of synthetic data, the VIM package for visualization and imputation of missing values and the package robCompositions for robust analysis of compositional data. He is author of the books Statistical Simulation in Data Science with R (Packt, 2016) and Statistical Disclosure Control (Springer, 2017).
Date de parution : 11-2018
Ouvrage de 280 p.
15.5x23.5 cm
Thème d’Applied Compositional Data Analysis :
Mots-clés :
Compositional data; Applications of compositional data analysis; Multivariate statistical methods; Robust statistics; Statistical environment R; Statistical methodology for compositional data; R package robCompositions; Analyzing compositional data using R; Methods for high-dimensional compositional data; Compositional tables; CoDa