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Titlebook: Applied Compositional Data Analysis; With Worked Examples Peter Filzmoser,Karel Hron,Matthias Templ Book 2018 Springer Nature Switzerland A

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發(fā)表于 2025-3-21 19:24:57 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Applied Compositional Data Analysis
期刊簡稱With Worked Examples
影響因子2023Peter Filzmoser,Karel Hron,Matthias Templ
視頻videohttp://file.papertrans.cn/160/159681/159681.mp4
發(fā)行地址Fills the gap in the existing literature by providing a practical approach to compositional data analysis.Presents a concise and easy-to-interpret methodology which guarantees a scale invariant analys
學(xué)科分類Springer Series in Statistics
圖書封面Titlebook: Applied Compositional Data Analysis; With Worked Examples Peter Filzmoser,Karel Hron,Matthias Templ Book 2018 Springer Nature Switzerland A
影響因子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.
Pindex Book 2018
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Fundamentals of Room Acoustics,aken as first option. In addition, also other alternatives are considered: symmetric pivot coordinates, which are suitable for a bivariate analysis, and particularly balance coordinates, which are interpretable in the sense of balances between groups of compositional parts. They can be intuitively c
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Propagation and Radiation of Sound,e displayed with the traditional ternary diagram. One just needs to be aware that the data to be visualized are driven by the Aitchison geometry, and thus there is the danger of misleading conclusions from this graphical tool. The overall preferred option is to display compositional data in any inte
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Yijun Yu,Arosha Bandara,Bashar Nuseibeh robustly estimated, is taken. For clustering observations (compositions), no particular methodological peculiarities occur; basically, any orthonormal logratio coordinates serve well for this purpose. In this chapter, some of the most popular methods are described in more detail: hierarchical clust
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https://doi.org/10.1007/978-3-319-91683-5ariates, the case of real response and compositional explanatory variables, regression between two compositions, and finally also regression between the parts within one composition. A further important task is considered: variable selection of relevant covariates by forward and backward selection.
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Ahmad Faiz Zubair,Mohd Salman Abu Mansory just be related to noise, is nearly impossible. Accordingly, it is meaningful here to consider even the elemental information, contained in pairwise logratios, to build up a relevant method for marker identification or for the detection of cell-wise outliers. The latter one can be used to reveal w
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