Ricerc@Sapienza

Inference for continuous time multi-state models presents considerable computational difficulties when the process is only observed at discrete time points with no additional information about the state transitions. In particular, when transitions between states may depend on the time since entry into the current state, and semi-Markov models should be fitted to the data, the likelihood function is neither available in closed form. In this paper we propose a Markov Chain Monte Carlo algorithm to simulate the posterior distribution of the model parameters.

Traditional Bayesian quantile regression relies on the Asymmetric Laplace (AL) distribution due primarily to its satisfactory empirical and theoretical performances. However, the AL
displays medium tails and it is not suitable for data characterized by strong deviations from the Gaussian hypothesis. An extension of the AL Bayesian quantile regression framework
is proposed to account for fat tails using the Skew Exponential Power (SEP) distribution. Linear and Additive Models (AM) with penalized splines are considered to show the