Multivariate Simplex Regression Model (Inference, Diagnosis, Application)
Multivariate Simplex regression model, inference, diagnosis and applications
In the literature, there are well-established models to analyze measurable variables in the open interval (0.1) that represent rates, proportions or index; Among them are the models associated with the Beta and Simplex distributions. In practice, there is a need to have multivariate models, in particular the bivariate case. In this sense, the main objective of this work is to propose the Multivariate Simplex regression model (MRSM) via the copula function. Estimators for the parameters are found via the maximum likelihood (MV) method and, via a simulation study, their asymptotic behavior is studied. A diagnostic analysis such as: residual analysis and global influence (generalized Cook’s distance and likelihood departure) are developed, with the aim of identifying possible atypical and/or influential points and suitability of the model to the data. Finally, the results are applied to a set of real data to exemplify the developed methodology.