In this paper, we propose a novel and efficient two-stage variable selection approach for sparse GLARMA models, which are pervasive modelling discrete-valued time series. Our consists in iteratively combining the estimation of autoregressive moving average (ARMA) coefficients models with regularized methods designed performing regression Generalized Linear Models (GLM). We first establish consi...

We consider the variable selection problem when response is subject to censoring. A main particularity of this context that information content sampled units varies depending on censoring times. Our approach based model where all 2k possible models are entertained and we adopt an objective Bayesian perspective choice prior distributions a delicate issue given well-known sensitivity Bayes factor...