Factor Analysis of Compositional Data with a Total

Carles Barceló-Vidal () and Josep Antoni Martín-Fernández ()
Additional contact information
Carles Barceló-Vidal: Campus Montilivi, Edif. P4, University of Girona, Department IMAE
Josep Antoni Martín-Fernández: Campus Montilivi, Edif. P4, University of Girona, Department IMAE

A chapter in Advances in Compositional Data Analysis, 2021, pp 125-142 from Springer

Abstract: Abstract The sample space of a manifest random vector is of crucial importance for a latent variable model. Compositional data require an appropriate statistical analysis because they provide the relative importance of the parts of a whole. Any statistical model including variables created using the original parts should be formulated according to the geometry of the simplex. Methods based on log-ratio coordinates give a consistent framework for analyzing this type of data. Here, we introduce an approach that includes both the orthonormal log-ratio coordinates and an auxiliary variable carrying absolute information and illustrate it through the factor analysis of two real datasets.

Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-71175-7_7

Ordering information: This item can be ordered from
http://www.springer.com/9783030711757

DOI: 10.1007/978-3-030-71175-7_7

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().