Giovanna Nappo

Kendall distributions and level sets in bivariate exchangeable survival models

For a given bivariate survival function F@?, we study the relations between the set of the level curves of F@? and the Kendall distribution. Then we characterize the survival models simultaneously admitting a specified Kendall distribution and a specified set of level curves. Attention will be restricted to exchangeable survival models.

On the Moments of the Modulus of Continuity of Itô Processes

The modulus of continuity of a stochastic process is a random element for any fixed mesh size. We provide upper bounds for the moments of the modulus of continuity of Itô processes with possibly unbounded coefficients, starting from the special case of Brownian motion. References to known results for the case of Brownian motion and Itô processes with uniformly bounded coefficients are included. As an application, we obtain the rate of strong convergence of Euler–Maruyama schemes for the approximation of stochastic delay differential equations satisfying a Lipschitz condition in supremum norm.

A filtering problem with counting observations:error bounds due to the uncertainty on the infinitesimal parameters

Let(X Y) be a pure jump Markov process with discrete state space. Let the state X be not bservable and the observation Y be accounting process. We are interested in the filter of X given Y and initsdependence on the model. More precisely we compare this filter with the filter of another system which differs from the previous one only by the infinitesimal parameters and the initial distribution, and we give anexplicit bound for the distance in variation norm between the two filters. Finally we use this bound to examine how much a discrete time approximation procedure is affected by a slight error in the model and, in a special case, to examine the error due to the use of a finite state space model instead of an infinite one

A filtering problem with counting observations: approximation with error bounds

We consider a pure jump Markov process (Xt Yt ) with discrete state space. We suppose that the state Xt is not observable and that the observation Yt is a counting process. We construct an approximation for the filter of Xt given (Ys s ≤ t), by means of a family of piecewise constant processes, depending on the value of Yt and on the time discretization parameter. Moreover we give an explicit error bound for the convergence of the scheme

Convergence in Nonlinear Filtering for Stochastic Delay Systems

We study an approximation scheme for a nonlinear filtering problem when the state process

Approximation of Nonlinear Filters for Markov Systems with Delayed Observations

X

Ordering properties of the TTT-plot of lifetimes with schur joint densities

is the solution of a stochastic delay diffusion equation and the observation process is a noisy function of

Nonlinear filtering for stochastic systems with fixed delay: Approximation by a modified Milstein scheme

X(s)

Nonlinear Filtering for Markov Systems with Delayed Observations

for

A Reaction-Diffusion Model for Moderately Interacting Particles

s\in [t-\tau,t]