Compositional Data Analysis: Where Are We and Where Should We Be Heading?

By J. Aitchison, Juan José Egozcue. 2005

378 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/540fbdcdbc5e3b9934b8202b1579d2f8ccc80767

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Citations in the corpus

X‐ray fluorescence core scanning of wet marine sediments: methods to improve quality and reproducibility of high‐resolution paleoenvironmental records (2012, positive): “In addition, log-ratios allow the application of multivariate statistics (Aitchison and Egozcue 2005; Weltje and Tjallingii 2008).”
Variable Selection in Compositional Data Analysis Using Pairwise Logratios (2018, methodology): “Data Set 2 (Aitchison 2005) Activity pattern of a statistician: 20 days, 6 activities te =Teaching; co consultation; ad administration; re Research; ot other wakeful activities; sl sleep The ALRs with respect to sleep (sl) give the best agreement—the Procrustes correlation (between full space…”
Colours and Cocktails: Compositional Data Analysis 2013 Lancaster Lecture (2014, background): “…Analysis (hereafter referred to as the Principles) including, in particular, subcompositional coherence, (for recent summaries of the Principles see Aitchison & Egozcue 2005; Egozcue 2009; Egozcue & PawlowskyGlahn 2011) and (iii) Aitchison’s (2003) dismissal of the then known alternative…”
Outlier Detection for Compositional Data Using Robust Methods (2008, background): “The set of all closed observations with positive values, denoted as SD, forms a simplex sample space, a subspace of RD. Convenient operations on the simplex and their properties for dealing with compositions were summarized in Aitchison and Egozcue (2005).”
Tools for compositional data with a total (2015, background): “Looking ahead, one of the central issues in compositional data analysis is subcompositional coherence which raises the question as to how subcompositional coherence is reflected in R+ and T (Aitchison and Egozcue, 2005).”
Regression Analysis of Multivariate Fractional Data (2016, methodology): “…(2008), is to assume that shares 4This method has been widely used in fields like geology, pedology, geochemistry, and biology (see the survey by Aitchison and Egozcue, 2005) and political science (see, e.g., Katz and King (1999). follow a multivariate normal distribution truncated at the…”
Exploring Compositional Data with the CoDa-Dendrogram (2011, background): “These points match the so called principles of compositional data analysis (Aitchison, 1986; Aitchison and Egozcue, 2005; Egozcue, 2009).”
A latent Gaussian model for compositional data with zeros (2008, background): “These ideas have subsequently been developed into a comprehensive set of statistical tools (Aitchison and Egozcue, 2005), and the theoretical and practical aspects of compositional data analysis remain active fields of methodological research (e.g. Egozcue et al. (2006) and von Eynatten et al.…”
Inferring Correlation Networks from Genomic Survey Data (2012, background): “Complementary approaches, where zeros are treated differently than non-zero values, are substantially more challenging, and are the subject of ongoing research [22].”
Model averaging and muddled multimodel inferences. (2015, background): “Indeed, this has been the motivation for the development of specialized transformations for compositional predic- tors based on the log ratio transformation (Aitchison 1986, Aitchison and Egozcue 2005) such as the isometric log ratio (Hron et al. 2012).”
Reply to “On the Harker Variation Diagrams; …” by J.A. Cortés (2009, methodology): “The principles of compositional data analysis were established by Aitchison (1986) and they were inspired more by the experience in different scientific fields, like geology, than by previous purely statistical contributions.”
Groups of Parts and Their Balances in Compositional Data Analysis (2005, methodology): “Compositional data analysis, as introduced by Aitchison (1982, 2003a), is based on ratios of parts, and this is the essence of a deeper understanding of the nature of these type of data.”
Spatial Regression Modeling for Compositional Data With Many Zeros (2013, methodology): “However, much effort has been devoted in the last 30 years to understanding the nature of such data and developing appropriate methods for their analysis, as discussed in, e.g., Aitchison and Egozcue (2005).”