In this paper we analyse the problem of the modelling of individual transitions in presence of an incomplete sampling scheme.

We analyse the model in which the latent durations Ti are i.i.d. generated by a distribution F. The statistician observes Yi= min (ti, Ci) and Ai=1I {Ti <= Ci} where ci is a censoring time. The prior probability on F is a Dirichlet process Di (n0F0).

This paper proposes to use a semi parametric regression method, named Sliced Inverse Regression (SIR hereafter), to analyse ambulatory blood pressure monitoring data.

We consider truncated processes, both in discrete and continuous time, and study their dynamic properties. When the underlying process is a diffusion process, we derive the infinitesimal generator of its truncated counterpart. This result is the basis for the estimation of the drift and...

We propose a new approach to tail analysis for data featuring high degrees of leptokurtosis. Heavy tails can typically be found in financial series, like stock returns or durations between trade arrivals. Our method of tail assessment consists in fitting selected pseudo-models to varying subsets...

We study inference in continuous and discrete processes and propose new estimation procedures. Our approach consists on truncating the initial process what simplifies the estimation while preventing all relevant properties of the infinitesimal generator. For diffusion processes, nonparametric...