We propose a nonparametric estimation theory for the occupation density, the drift vector, and the diffusion matrix of multivariate diffusion processes. The estimators are sample analogues to ...

Robust estimation and risk minimisation in stochastic processes represent a central domain in modern statistical research. These methods are designed to ensure that statistical predictions remain ...

Stochastic processes provide a rigorous framework for modelling systems that evolve over time under uncertainty, while extremal theory offers the tools for understanding the behaviour of rare, ...

This paper studies a problem of Bayesian parameter estimation for a sequence of scaled counting processes whose weak limit is a Brownian motion with an unknown drift. The main result of the paper is ...

The identities or bounds that relate information measures (e.g., the entropy and mutual information) and estimation measures (e.g., the minimum means square error ...

A fundamental component in the modeling of a financial risk exposure is the estimation of the probability distribution function that best describes the true data-generation process of independent and ...

A new process control method uses a special mathematical structure to accurately estimate the internal process variables of a system, even when external sensors are damaged. Be it nuclear power plants ...