Abstract
The aim of this work is to propose a new multivariate regression model for
compositional data, i.e., vectors of proportions. It is based on a mixture of Dirichletdistributed
components and it enables many relevant properties for compositional
data as well as accounting for positive correlations. Despite the complexity of the
model, its special mixture structure provides a greater flexibility and a richer parameterization
than the standard Dirichlet regression (DirReg) model and, moreover,
guarantees its identifiability. We illustrate the performance and the goodness of fit
of our new model through an application to the last Italian elections data.
| Lingua originale | Inglese |
|---|---|
| Pagine | 817-822 |
| Numero di pagine | 6 |
| Stato di pubblicazione | Pubblicato - 2019 |
| Evento | Smart Statistics for Smart Applications - SIS 2019 - Milano, Italy Durata: 1 gen 2019 → … |
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| ???event.eventtypes.event.conference??? | Smart Statistics for Smart Applications - SIS 2019 |
|---|---|
| Città | Milano, Italy |
| Periodo | 1/01/19 → … |
Keywords
- bayesian inference
- dirichlet distribution
- mixture model
- simplex