Parameter estimation in diffusion processes from discrete observations up to a first‐passage
time is clearly of practical relevance, but does not seem to have been studied so far. In …

In this article, we consider the development of unbiased estimators of the Hessian, of the log-
likelihood function with respect to parameters, for partially observed diffusion processes …

Parameter estimation in Markov processes from discrete observations up to the first-hitting
time of an upper threshold is clearly of practical relevance, but does not seem to have been …

We consider a sample\left {T_n\right\} _ 1 ≦ n ≦ N of iid times and we interpret each item as
the first-passage time (FPT) of a diffusion process through a constant boundary. The …

Everyone would certainly agree that mathematics is becoming an indispensable tool of
investigation in the biological sciences. What is probably less evident is that in turn …

Several estimating functions for discretely observed diffusion processes are reviewed. First
we discuss simple explicit estimating functions based on Gaussian approximations to the …

We propose a method for estimating first passage time densities of one-dimensional
diffusions via Monte Carlo simulation. Our approach involves a representation of the first …

The statistical problem of parameter estimation in partially observed hypoelliptic diffusion
processes is naturally occurring in many applications. However, because of the noise …

This work is a survey of one-dimensional diffusion models for the membrane potential in a
single neuron. While some biological modeling issues are discussed, the emphasis is on the …

Estimation of parameters in stochastic processes has been thoroughly investigated for
decades and the asymptotic properties of the estimators are known. However, reaching the …