Joint estimation of conditional quantiles in multivariate linear regression models with an application to financial distress
This paper proposes a maximum likelihood approach to jointly estimate marginal conditional quantiles of multivariate response variables in a linear regression framework. We consider a slight reparameterization of the multivariate asymmetric Laplace distribution proposed by Kotz et al. (2001) and exploit its location–scale mixture representation to implement a new EM algorithm for estimating model parameters.