id: 05946034
dt: j
an: 05946034
au: Soffritti, Gabriele; Galimberti, Giuliano
ti: Multivariate linear regression with non-normal errors: a solution based on
    mixture models.
so: Stat. Comput. 21, No. 4, 523-536 (2011).
py: 2011
pu: Springer, Dordrecht
la: EN
cc: 
ut: EM algorithm; model selection criterion
ci: 
li: doi:10.1007/s11222-010-9190-3
ab: Summary: In some situations, the distribution of the error terms of a
    multivariate linear regression model may depart from normality. This
    problem has been addressed, for example, by specifying a different
    parametric distribution family for the error terms, such as
    multivariate skewed and/or heavy-tailed distributions. A new solution
    is proposed, which is obtained by modelling the error term distribution
    through a finite mixture of multi-dimensional Gaussian components. The
    multivariate linear regression model is studied under this assumption.
    Identifiability conditions are proved and maximum likelihood estimation
    of the model parameters is performed using the EM algorithm. The number
    of mixture components is chosen through model selection criteria; when
    this number is equal to one, the proposal results in the classical
    approach. The performances of the proposed approach are evaluated
    through Monte Carlo experiments and compared to the ones of other
    approaches. In conclusion, the results obtained from the analysis of a
    real data set are presented.
rv: