Regression Modelling Analysis on Compositional Data
Handbook of Partial Least Squares(2010)
Abstract
In data analysis of social, economic and technical fields, compositional data is widely used in problems of proportions to
the whole. This paper develops regression modelling methods of compositional data, discussing the relationships of one compositional
data to one or more than one compositional data and the interrelationship of multiple compositional data. By combining centered
logratio transformation proposed by Aitchison (The Statistical Analysis of Compositional Data, Chapman and Hall, 1986) with
Partial Least Squares (PLS) related techniques, that is PLS regression, hierarchical PLS and PLS path modelling, respectively,
particular difficulties in compositional data regression modelling such as sum to unit constraint, high multicollinearity
of the transformed compositional data and hierarchical relationships of multiple compositional data, are all successfully
resolved; moreover, the modelling results rightly satisfies the theoretical requirement of logcontrast. Accordingly, case
studies of employment structure analysis of Beijing’s three industries also illustrate high goodness-of-fit and powerful explainability
of the models.
MoreTranslated text
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined