Dirichlet regression models can be used to analyze a set of variables lying
in a bounded interval that sum up to a constant (e.g., proportions, rates,
compositions, etc.) exhibiting skewness and heteroscedasticity, without
having to transform the data.
There are two parametrization for the presented model, one using the common
Dirichlet distribution's alpha parameters, and a reparametrization of the
alpha's to set up a mean-and-dispersion-like model.
By applying appropriate link-functions, a GLM-like framework is set up that
allows for the analysis of such data in a straightforward and familiar way,
because interpretation is similar to multinomial logistic regression.
This paper gives a brief theoretical foundation and describes the
implementation as well as application (including worked examples) of
Dirichlet regression methods implemented in the package DirichletReg (Maier,
2013) in the R language (R Core Team, 2013). (author's abstract) / Series: Research Report Series / Department of Statistics and Mathematics
| Identifer | oai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:4077 |
| Date | 18 January 2014 |
| Creators | Maier, Marco J. |
| Publisher | WU Vienna University of Economics and Business |
| Source Sets | Wirtschaftsuniversität Wien |
| Language | English |
| Detected Language | English |
| Type | Paper, NonPeerReviewed |
| Format | application/pdf |
| Relation | http://epub.wu.ac.at/4077/ |
Page generated in 0.002 seconds